ViaMind Journal (English)

🧾 The AI Bill: the Problem Almost Nobody Is Talking About

Tue, 14 Jul 2026 00:00:00 +0000

Over the last two years we have heard the same speech:

“AI will replace millions of jobs.”

“Companies will become much more efficient.”

“You will be able to do the work of two or three people.”

And, being honest, part of that is already happening. I myself would not have been able to build NeuraPRO in the time I did without Claude, Cursor, GitHub Copilot, and ChatGPT. Today I use AI every day in a multinational company to automate processes, develop internal tools, and manage projects. In addition, I have spent months studying how it works internally: agents, MCP, Skills, and Subagents.

The more I learn, the more convinced I am that we are living through a technological revolution comparable to the arrival of the Internet. But I am also starting to believe that we are looking at the problem from the wrong angle.

It is not capability. It is not security. It is not model quality.

It is something much simpler:

Who pays the bill?

When AI stops being a demo

A few months ago we had practically free access to Claude at my work. And exactly what one would expect happened: we started building. A lot.

In my case I developed a Planner fully integrated with Jira, Confluence, and our internal portal. Today I can create complete projects, maintain dependencies between tasks, reorganize calendars without breaking the plan, assign engineers, create tasks automatically, update Jira with one click, calculate effort, measure overall progress, and export the plan to share it with other teams.

It still has a lot to improve, but it already solves real problems. And it will probably end up being used far beyond my team.

Then everything changed

As the weeks went by, limits started to appear: internal talks, best practices, emails recommending prompt optimization, using cheaper models whenever possible, and thinking before launching complex queries.

And that is when I understood something.

It was not a technical problem. AI kept working exactly the same. It was not a security problem.

It was a cost problem.

And I think many companies did exactly the same thing: first they allowed teams to experiment freely and then they analyzed the data. How much was used? How much time was really saved? How much did it cost?

If I had had to make that decision as a technology leader, I probably would have done exactly the same thing. Because there is no better way to understand a technology than seeing it working in production.

It is not just a perception

My experience does not seem to be an isolated case.

Over the last few months, several reports have appeared showing that many companies are starting to control AI consumption much more closely. Some studies indicate that more and more organizations are imposing budgets, usage quotas, or promoting cheaper models because operating costs are growing much faster than expected.

At the same time, companies like Microsoft, Google, Meta, OpenAI, and Anthropic continue to announce investments of tens of billions of dollars in infrastructure to support the growing demand for AI models.

Technology is advancing, but making it run remains extraordinarily expensive.

Let us do a simple exercise

Imagine a company with 5,000 employees. Not all of them develop software; many simply use AI for their daily work: reading documentation, preparing presentations, summarizing meetings, analyzing contracts, consulting internal procedures, writing code, or replying to emails.

Now imagine that each person makes only 20 relevant queries per day. Not simple questions, but queries where AI must read documentation, understand Jira tickets, review Confluence pages, analyze files, use tools through MCP, or reason before answering.

Each one consumes context, processing, and tokens.

Multiply that by five thousand people, then by twenty working days, and then by twelve months.

Suddenly AI stops being simply a productivity tool. It becomes a new expense line that can represent millions of dollars per year.

And that bill keeps growing as we use increasingly sophisticated agents.

Agents are impressive…

…but they also consume a lot.

Over the last few months, I have been studying in more depth how modern agents work, not only from a practical point of view, but also from what happens behind the scenes.

An agent does not make a single query: it reads documentation, consults Jira, searches for information in Confluence, invokes tools through MCP, consults other specialized agents, reasons, queries again, generates tasks, and validates results.

Each of those steps consumes tokens again. Many more than most people imagine.

As an engineer, I find it incredible.

As a Project Manager, I inevitably think something else:

Is it sustainable when an entire organization starts working like this every day?

Is it really cheaper than a person?

This is probably the question I have been asking myself the most lately.

For a long time I heard an idea that seemed obvious:

“It will be much cheaper to replace people with AI.”

Today I am no longer so sure.

Not because AI is not capable —it is—, but because the doubt is somewhere else.

If an experienced engineer solves a task in one hour, the cost is known. If an agent needs to analyze hundreds of pages of documentation, go through tickets, consult several tools, reason for several minutes, and generate an output, the cost also exists. And we are still learning which of the two models is more profitable depending on the type of work.

I think that is where the real debate is: not only asking what AI can do, but how much it costs to do it millions of times every day.

The biggest lesson all this left me

Curiously, the best result I got was not asking more questions, but no longer needing them.

Building my Planner consumed a lot of tokens, but now that it exists, much of that cost disappeared. The logic was implemented. I no longer need to constantly ask a model how to reorganize tasks or calculate dependencies: the system simply does it.

And that completely changed the way I see AI.

Maybe the greatest value is not in talking endlessly with a model.

Maybe it is in using that intelligence to build tools that later work almost by themselves.

My reflection

I do not think we are living through a technology bubble. AI works. I see it every day. I used it to build NeuraPRO, I use it in my work, and I keep studying it because I am convinced it will change the way we work.

But I do believe we are living through a stage where expectations are moving faster than reality. Today we talk a lot about replacing people and very little about costs, return on investment, or sustainability.

And I increasingly wonder how human work will evolve in this new scenario. How we will find the balance between productivity, technology, costs, and people.

Because one thing is proving that AI can do a job. Another very different thing is proving that it can do it every day, for thousands of people, for years, at a cost a company is willing to assume.

Maybe that is why we are seeing more and more organizations promoting smaller models, optimizing prompts, establishing budgets, and teaching people to spend fewer tokens.

Not because AI has failed.

Quite the opposite.

Because for the first time it stopped being a demonstration and started being used for real.

The next AI revolution will no longer depend only on creating smarter models.

It will depend on making that intelligence economically sustainable.

And that will probably be the most important innovation of all.

Related reading:

✍️ Claudio from ViaMind

“Dare to imagine, create and transform.”

🤖 Are We Entering a Silent Labor Crisis?

Thu, 25 Jun 2026 00:00:00 +0000

A few days ago I found myself reflecting on something I have been observing for years, but only now started to connect.

When I arrived from Chile to the Netherlands in the middle of the pandemic, the labor market was very different. At that time, companies were still hiring engineers, project managers, architects, specialists, and external consultants to move complex projects forward. There was room for contractors, service companies, and independent professionals. I myself was hired from Chile to work in Europe as a contractor. If more work appeared, the solution seemed quite simple: hire more people.

Today, barely six years later, I have the feeling that the conversation has changed completely. And the most curious thing is that the work did not disappear. People did.

Projects still exist. Clients still call. Systems still fail. Operations still run.

The difference is that now we expect fewer people to do the same work that entire teams used to do before. And often even more.

From growth to cost reduction

For much of my professional career, I felt that companies competed to attract talent. The conversation revolved around how to execute more projects, how to innovate faster, or how to find specialists capable of solving complex problems.

Today I perceive something different. The priority seems to be efficiency. Reducing costs. Optimizing structures. Consolidating teams. Outsourcing functions. Automating processes. And, above all, doing more with less.

I have seen entire organizations transform in just a few years. Companies that once maintained large internal teams now depend on global providers. Entire departments were reduced. Specialized roles disappeared. And many times the work was simply absorbed by those who remained.

The workload did not disappear. It only changed owners.

Maybe that is why one of the phrases I hear most often lately among colleagues is:

“I am exhausted.”

Not because there is less work. But because the same work is being distributed among fewer people.

The promise of artificial intelligence

I am fascinated by artificial intelligence. I use it every day. I am studying it, building projects with it, and constantly learning about its capabilities. It would be absurd to deny the impact it is having.

But I also believe that many times we are confusing potential with reality.

I often hear that AI will solve operational problems, replace repetitive tasks, increase productivity, and allow companies to work with smaller teams. And part of that is probably true.

What I am not so sure about is whether we are being equally realistic about its limits.

In my experience, AI is more like a horse than a driver. It has strength. It has speed. It can multiply a person’s capacity. But it needs direction. It needs judgment. It needs context. It needs someone who knows where it is going.

AI does not execute a strategy by itself. It does not lead teams. It does not assume responsibility. It does not truly understand the consequences of a decision. At least not today.

That is why I worry when some organizations seem to be making decisions as if technology had already solved problems it has not yet solved.

When experience stops mattering

Not long ago I remembered something from my years studying telecommunications. Back then, talking about Cisco meant talking about technical excellence. If you had a complex problem, you quickly reached an expert who knew the product in depth. If that person could not solve it, the case was escalated to someone even more experienced.

Joining a call with those engineers was almost like attending a masterclass.

Today the experience is often different. Longer processes. More forms. More layers. More escalations. More bureaucracy. And many times less specialized knowledge at the first point of contact.

This is not a problem exclusive to Cisco. I see it across many industries. The obsession with efficiency has displaced part of the value that used to be assigned to experience. And when that happens, quality eventually suffers.

Maybe costs go down. But something difficult to measure in a spreadsheet is also lost: accumulated knowledge.

What really worries me

Technology does not worry me. Technology has always changed the world. It has done so for centuries. I also do not believe we should stop the advance of artificial intelligence. That would be absurd.

What worries me is something much simpler.

I do not understand what society will look like if a significant part of human work disappears. Maybe there are economists, academics, or leaders who have answers for that. I do not. I only have questions.

Because a society does not function only because of technology. It functions because people work. They generate income. They consume. They pay taxes. They form families. They build communities.

If one day AI replaces millions of jobs, how will that balance be sustained?

Who will buy the products?

Who will pay the taxes?

Who will fund the systems that today depend on the work of millions of people?

Maybe an answer exists. Maybe we have not found it yet.

Looking ahead

I am forty years old. I still have more than twenty years of professional career ahead of me. And if I, after years working in technology, have doubts about what the labor market will look like in a decade, I can imagine how someone who is just starting must feel.

Or what it will be like for our children.

Maybe artificial intelligence will lead us to a better society. I hope so.

But when I look at what is happening today in many industries, I see smaller teams, fewer opportunities, slower hiring processes, and a level of uncertainty that did not exist with this intensity before.

That is why I believe the conversation should not focus only on how fast technology is advancing. We should also ask what kind of society we want to build around it.

Because a company can operate with fewer workers. But a society is much more complex than a spreadsheet.

And that, at least for me, remains the most important question of all.

✍️ Claudio from ViaMind

“Dare to imagine, create and transform.”

🧠 NeuraPRO: My MVP Worked. My Customers Didn't.

Mon, 15 Jun 2026 00:00:00 +0000

About a year and a half ago I started building NeuraPRO.

The idea seemed fairly logical: create a platform that would help small businesses manage inventory, production, sales, and daily operations in a more organized way. For years I had seen businesses running on spreadsheets, notebooks, WhatsApp messages, and a lot of information stored only in people’s experience. I thought I could help solve part of that problem.

As an engineer and project manager, I approached the challenge in the way I knew best: by building. For months I worked on the architecture, prepared development, preproduction, and production environments, configured domains, integrated payments, improved the mobile experience, and redesigned the interface several times. I also built a user portal separate from the main application, simplified access to avoid multiple logins, and spent a lot of time on details that probably no user would consciously notice, but that were important to me.

Looking back, I realize that much of my energy was focused on making sure the product was ready to grow when the time came. And technically it was.

The application did not only work. It works. NeuraPRO is still alive, the platform is still operational, still evolving, and still has a clear path for agriculture and businesses that need to organize inventory, production, and sales. After more than a year of work, I felt I had built a solid foundation on which I could keep developing new features.

Then came the moment to launch it.

The launch

My initial strategy was relatively simple. I started with Meta Ads campaigns using small but consistent budgets. The idea was to validate whether there was real market interest: people saw the ads, clicked, and landed on the NeuraPRO website.

On top of that, I offered three free months of use. No credit card, no commitment, and no risk. My reasoning seemed logical: if the problem existed and the solution was useful, people would start using it.

At least that was the theory.

What the data showed

The clicks came. The visits came. People did indeed enter the site. But when I analyzed their behavior, the same pattern appeared again and again: they entered, explored a little, and left.

At first I thought it was a technical problem. Maybe the page was slow, maybe the registration process was too long, maybe the mobile experience could be improved, or maybe an important feature was missing. So I did what many engineers do when something does not work: analyze, measure, optimize, change, measure again, and repeat.

In parallel, I learned something that was not in my comfort zone: trying to sell the product. I created carousels for Meta Ads, tested different messages, adjusted copy, reviewed which images attracted more attention, and slowly started to understand how people’s behavior changes depending on a sentence, a promise, or a first impression.

I also requested videos and images from external collaborators, tested different formats, and learned that communicating a product is almost a product of its own. It is not enough to have an application that works. You have to explain in seconds why it should matter to someone.

I had to learn how to manage campaigns, how to look at landing page visits, how to interpret small signals, and how to improve the page so that the person arriving there could understand faster which problem NeuraPRO solves. I even built dashboards inside the portal to observe those data points more clearly: how many people arrived, where they came from, what they did, where they stopped, and what I could learn from that behavior.

That opened another world. Marketing, data, product, and human behavior started to mix. And although I am still far from having all the answers, today I understand much better that launching a product is not simply putting it online. It is building a complete system around attention, trust, message, timing, and continuous learning.

That is why I keep exploring this path. I keep thinking about new marketing strategies, keep adjusting the landing page, and keep observing what captures customer attention and what does not. At some point I will probably do a second launch, better prepared, with better materials, better data, and a clearer story.

Because the first time I did many things on the fly. And that was also part of the learning.

But the more I looked at the data, the more obvious an uncomfortable possibility became.

The problem was probably not in the software.

The lesson that was hardest to accept

I think I made a fairly common mistake among those of us who build technology products: I assumed that the main problem my potential customers had was the lack of a tool.

Today I think I was looking at the situation from the wrong perspective. Many people are not actively looking for new software. They do not spend time comparing SaaS platforms, evaluating features, or researching digital transformation processes. They are focused on keeping their business running, serving customers, solving operational problems, and making it to the end of the day.

And changing the way someone works is much harder than developing an application.

That was probably the most important lesson of this whole process. I saw an obvious opportunity for improvement. But for many potential users, the problem simply did not have the same level of urgency that it had for me.

And that difference changes everything.

The second attempt

As a second strategy, I decided to rely on something closer: my own network. I started talking with friends, acquaintances, and people connected to different industries. I thought maybe the problem was not the product, but trust. Perhaps a direct recommendation or a more personalized onboarding could lead to a different kind of adoption.

I got more signups, more conversations, and more initial interest. But the final result was quite similar. Some people tried the platform. Others even completed the onboarding process. However, after some time, they stopped using it.

The question remained the same: why?

The truth is that I still do not have a definitive answer. And I think part of the learning is accepting that some answers require more time than one would like.

NeuraPRO is still here

Something important I want to make clear is that NeuraPRO is not dead. It was not abandoned. It is not a forgotten project in a repository.

The platform is still running, the infrastructure is still operational, and the roadmap continues to move forward. The agricultural line is also still open, and it was an important part of NeuraPRO’s origin. I still see value there: producers, distributors, warehouses, stock, sales, and processes that often continue to run with scattered information.

What changed was my focus. Today I dedicate less energy to adding features and more energy to understanding problems. Less time building and more time observing. Less time programming and more time talking with people who truly live the problem I am trying to solve.

Because after this process I understood something important: adding features is relatively easy. Deeply understanding an industry is much harder.

From a multi-industry solution to understanding one industry

When I started NeuraPRO, I imagined a platform capable of adapting to multiple types of businesses. And technically, that is still true.

However, over the last few months I have started working with a friend who manages several bakeries in Chile, and that experience has been completely different. For the first time I feel I am spending more time understanding the business than building software.

Our conversations are no longer centered around screens, reports, or databases. We talk about raw material waste, production control, costs, margins, operational errors, daily processes, and real problems.

I have had many conversations trying to understand where he loses time, where he loses money, and what things truly cause pain in his daily operations. And the deeper we go, the more obvious something becomes that I probably should have understood from the beginning.

The best solutions are born when you deeply understand the problem before you start building.

The interesting part is that much of the difficult work is already done. The platform exists, the architecture exists, and the main backlog exists. Today, much of the effort is about adapting, simplifying, and adjusting what has already been built for a specific industry.

And that difference is enormous.

What AI changed along the way

There is another reason why I keep building. And it was probably never only about money.

Of course I would like to create something successful. Any entrepreneur would. But there is another motivation too: understanding how to work in a world where artificial intelligence is changing the way we create, learn, and solve problems.

During these years I discovered that my way of working adapts quite well to this new paradigm. I feel comfortable working with clear objectives, concrete actions, small tasks, and measurable results. AI did not replace that way of working. What it did was accelerate it.

That is why I have also started building tools for myself, tools designed to solve problems I face every day in my work. After all, who better than oneself to identify the frictions one experiences daily.

And perhaps that has been one of the most interesting lessons of this whole journey: today it is possible to build extremely personalized solutions for very specific problems, something that only a few years ago would have been much more difficult.

Something I did not expect

Interestingly, while I was working on NeuraPRO, many people began contacting me. Some were friends. Others were not. Most arrived with a similar idea:

“I have this problem in my business.”

“Do you think this could be automated?”

“Could a tool be built to solve this?”

It did not always turn into a project. But it did leave me with an interesting conclusion: the problems exist, the opportunities exist, and the ideas exist.

The hard part is not building.

The hard part is choosing correctly which problem is worth solving.

Final reflection

If someone asked me today what the main lesson of the last eighteen months was, I would probably answer something very different from what I would have said when I started.

It was not a technical lesson. It was not a lesson about architecture. It was not a lesson about artificial intelligence.

It was a lesson about people.

I learned that a good product does not guarantee adoption. I learned that good architecture does not guarantee customers. I learned that having more features does not necessarily create more value. And I learned that deeply understanding the problem is usually much more important than quickly developing the solution.

NeuraPRO is still here. The roadmap is still here. The ideas are still here. And I will probably keep building for a long time.

Only now I try to do it in a different order.

First understand the problem.

Then build the solution.

To follow the NeuraPRO story: the origin, the technical foundation and the road before the MVP.

✍️ Claudio from ViaMind

“Dare to imagine, create and transform.”

🎯 Project Management: Make Things Happen for Real

Wed, 10 Jun 2026 00:00:00 +0000

Years ago, during a conversation with one of my directors at VTR, I received advice that stayed with me.

“You have to make things happen.”

At the time it seemed like a simple phrase. Even obvious.

Over the years I understood that it probably sums up much of what managing complex projects really means.

Because managing projects is not about filling calendars, creating tickets, or sending emails. Managing projects is about making something actually happen in the real world.

And that difference is far more important than it seems.

The illusion of progress

In many projects there is a constant sense of forward movement.

Emails get sent. Meetings happen. Tickets get created. Dates get set. Plans get updated.

Everyone seems aligned. Everything seems to be moving.

But often that progress is an illusion.

I have seen projects where the change date was set weeks before there was clarity on who would do each task, which dependencies were resolved, or whether teams were truly ready to execute.

And when change day arrives, reality shows up.

An approval is missing. A configuration is missing. A validation is missing. A key person is missing.

Then the meeting drags on. The change gets postponed. The client waits. Teams lose time. And trust starts to erode.

The date existed. But the conditions for success did not.

What really makes a change happen

Over time I learned that a change is not ready because it appears on the calendar.

It is ready when the necessary conditions exist to execute it.

That involves several things.

Clear owners

Not teams. Not departments. Not areas.

People.

Every important task must have a specific owner. Someone who knows exactly what to do, when to do it, and what happens if something fails.

Diffuse responsibility is one of the biggest enemies of execution.

Dependencies identified and closed

Many problems do not appear during execution. They are created weeks earlier.

When someone assumes another person already finished something. When a validation was never done. When a prerequisite stayed pending because nobody was tracking it.

The more complex the environment, the more important it becomes to understand dependencies.

Real availability

I have heard phrases like this many times:

“Don’t worry, we will support you.”

But supporting is not enough.

I need to know who will be present. At what time. In what role. And for how long.

Availability must be confirmed before the change, not during the change.

Preproduction that reflects reality

This is probably one of the most important points.

Preproduction does not exist to prove everything works. It exists to discover what still does not work.

If the procedure we will run in production is different from what we tested in preproduction, then we are not really testing the change. We are testing something else.

And that creates one of the most dangerous risks in technology: a false sense of security.

In production environments, especially when real customers are using the services, surprises are expensive. They can impact revenue. They can affect SLAs. They can trigger penalties. They can damage customer trust.

That is why I have always believed preproduction should resemble as closely as possible the reality we will face afterward.

Forty-eight hours before

Something critical happens at that point.

Reviewing documents in silence is no longer enough. You have to reach out to people. Ask if everything is truly okay. Confirm resources, roles, and timing.

Do not assume that because something was said in a meeting two weeks ago, it is still true.

If doubt appears, if something is unclear, if open points remain, it is worth organizing a meeting two or three days before and honestly assessing whether the change should proceed.

In my experience, it is not worth rushing something that is not ready when the risk is very high. Sometimes the right call is to cancel or move the window.

Nobody wants that. It looks like a defeat. But it is not.

Sometimes, to move forward, you first have to take a step back.

And that does not apply only to work. It applies to any area of life.

Discipline does not mean bureaucracy

That instinct does not remove judgment.

You start to tell when a project is on the right track and when something does not quite fit.

Sometimes processes can be simplified. Sometimes calculated risks can be taken. Sometimes it is even reasonable to skip a minor step.

But when we talk about complex changes, multiple teams, interdependent systems, and distributed responsibilities, discipline stops being bureaucracy.

It becomes a tool to reduce uncertainty.

The difference between a smooth execution and a complicated night is usually built long before the change begins.

The real meaning of that phrase

After many years working on deployments, migrations, upgrades, and production changes, I still remember that phrase.

“Make things happen.”

Today I interpret it differently.

It does not mean pushing more meetings. It does not mean sending more emails. It does not mean creating more documentation.

It means creating the conditions necessary for execution to succeed before change day arrives.

Because in the end, calendars do not deliver projects. Prepared people do.

Related reading:

I Never Studied to Be a Project Manager. I Learned in Production. — hundreds of live changes and what no course teaches.
My View on Project Management — scope, governance, and disciplined execution.
When Jira Is Not Enough — planning what comes first when the bottleneck is not execution.
About me — career in telecom and project management across Chile and Europe.

✍️ Claudio from ViaMind

“Dare to imagine, create and transform.”

How many times have you seen a date on the calendar without the real conditions to execute?

Because in the end, it is not about having everything planned. It is about being ready when the moment arrives.

🛠️ When Jira Is Not Enough: How I Built My Own AI Planning System

Tue, 02 Jun 2026 00:00:00 +0000

The last months of the year have a particular rhythm in telecommunications. And this year more than ever.

When the FIFA World Cup is on the calendar, the whole sector knows there is a date that does not move: before it starts, everything has to be in production. What does not make it on time waits until after — and “after” means reaching year-end with the accumulated pipeline on top of you.

The result is the usual thing, but tighter: changes competing for the same windows, upgrades that depend on another project finishing first, migrations coordinated across several countries with different freezes and local teams with their own calendars — the same kind of friction I described when moving from a local team in Chile to coordination from Europe. Suddenly you are looking at a pipeline where everything is urgent, everything has dependencies, and in a few days you have to present the real status to management — without alarming them, but without hiding anything either.

The problem is not the amount of work. The problem is visibility. What comes first? What blocks what? Where is there a capacity conflict? Before answering any of those questions, you first have to build the picture. And that used to take days.

1. 🔍 The problem that lives between the tools

The tools are not missing. Jira for managing tasks, planners for organizing work, documentation that is theoretically up to date. The problem is that none of them tell you what happens over time — and in complex operations, that is exactly what you need to know.

Jira tells you what exists. Not when it goes, in what order, or what depends on what. It does not understand that if you have active projects in the backoffice that serves local services, you cannot run a complex upgrade of another component in parallel — because if something fails in production, nobody will know where the problem came from. That is not a minor detail. In production, traceability is everything.

Coordinating initiatives across several countries, with different platforms and local teams with their own rhythms and priorities, requires a view that no product on the market provides. Not a task list — a real read of what is in flight, what blocks what, where the real risk is, and how to communicate upward without alarming people and without hiding anything.

Then there are runbooks. A runbook is basically the step-by-step manual executed during a maintenance window — what the technical team does to implement a change in a controlled way. The problem is that over time they multiply: versions by country, numbered updates, variants by platform. Hundreds of Excel files that nobody knows for sure are current. Every window starts with hunting for the right file. It is a pain that has gone unsolved for years and has real consequences when something goes wrong.

2. 🛠️ What I built — and how it works

What I built has two parts that work together: a team portal and a planner.

The portal connects to Jira and gives visibility per project — from the EPIC down to the implementation task, with progress, planning, and status. But it goes beyond a Jira view. Each implementation task has its associated runbook, versioned, without Excel. There is a learnings section where what used to get lost in chats and emails is recorded. And you can export an executive summary with blockers and next actions, ready to share.

The planner is where I organize everything over time. I work with chain templates — basically a chain of tasks that defines everything that has to happen for something to be delivered. An upgrade follows its sequence: preprod, DR, prod batch 1, prod batch 2. A migration has its own. I can see several projects for the same country on the same timeline, move dates by dragging, and Jira updates on its own.

I built all of this with Claude — the same way I did with NeuraPRO — applying the same logic: understand the problem well before you start building.

Illustrative image with sample data only. It does not show real operational information and does not expose private data from any market.

And on top of all this, Claude. An EPIC in Jira can have dozens of tasks, phases, dependencies, and acceptance criteria. Before, structuring all of that in a project took hours. Now, with Claude integrated directly in Jira, I can take an EPIC, analyze it, and turn it into organized chains and tasks — with its hierarchy, phases, and structure ready to go. I review, adjust what does not match the team’s reality, and execute.

From there, that structure moves into the planner. The team can see work spread over time, start prioritizing, spot conflicts, and talk about real risks — not dates dropped on a calendar without context.

Today I use it myself as the team’s technical project manager. First I want to get the most out of it in this stage — there is a lot in the pipeline and this is the moment to test and adjust. The next phase is opening it to the team with role-based access: each engineer with visibility of their tasks, their runbooks, and their progress, without everything having to go through one person.

A real example

In one market we had three critical projects running at the same time: two platform upgrades and a migration. Three EPICs in Jira, same country, same period.

Management was not asking if a window was available. They were asking what goes first and what has the most impact on production if something goes wrong.

In the planner all three show up as chain templates for the same country, on the same timeline. That is where you see what Jira does not: an engineer assigned to tasks on different projects the same day, a backup environment clashing with another activity, or a complex operation scheduled before earlier stages are validated. Without that visibility, if something fails in production you do not know where it came from.

The logic to order them was simple: preprod for all three first, then validation with the teams, then DR leaving at least one week between chains, and production order defined by how each one went in the earlier stages. If something fails, there is clear traceability.

When the issue is one person’s capacity, I filter by owner and see it immediately. For the management report, I export from the portal — dates, risk, status per project — without building a presentation from scratch.

3. ⚡ What changed

Yes, I save time. But that is not the main point.

Before, a lot of energy went into building the picture: pulling data from Jira, cross-checking information from different sources, trying to visualize somewhere which projects are in flight, what state they are in, who owns what, and when things clash. That is not program management — it is assembly. And when it takes days to put together, it is already outdated.

Now that time exists for what matters: does this project go first or second? Does the team have real capacity this sprint or just dates on a calendar? Is the risk we are seeing in one initiative related to something that already happened in another market?

As a program manager, the value is not knowing where each task sits. It is understanding how they relate to each other, what blocks what, and being able to communicate that clearly — to the team and upward. For that, you need the full picture, always updated. Before, building that picture was a job on its own. Now it is the starting point.

4. 🤔 What I keep turning over

The tool exists, works, keeps improving. But there are two things I cannot ignore.

The first: the same reasoning I used here works in any industry where there are processes and coordination. A mid-size hardware store. A pharmacy. A bakery with several locations. Not the large companies with IT teams — the ones with some technology but no idea where to start with AI. With a small group of people who know how to use AI, think clearly, and have worked under pressure, I think you can do quite a lot. The limit is not technical.

The second is more uncomfortable: in two or three years, at certain points in the chain there will be fewer and fewer people needed. Not as a prediction — as something already happening. What I built in weeks used to need a team and several months.

The issue is not whether to automate. It is at which point someone still needs to be watching — and how that gets decided. Today everyone resolves it as they can. At some point that will have structure: roles, policies, defined responsibilities.

What I do see from the inside is that the industry is still waiting for this to resolve itself. Very little real action. Management in many cases is still in observation mode, without fully understanding that the moment to experiment has passed — we are in the moment to build. And those who did not start will be late to that conversation.

✍️ Claudio from ViaMind

“Dare to imagine, create, and transform.”

Also available in Spanish: Cuando Jira no alcanza.

🌐 AI & Telecom Trends – May 2026: Five Signals Worth Keeping on Your Radar

Tue, 26 May 2026 00:00:00 +0000

May has been eventful.

Chilean startups with real traction, Claude moving into Wall Street in ways ChatGPT still cannot replicate, and a couple of infrastructure and security signals the market is not discussing enough.

Here are five things worth keeping on your radar this week — especially if you work in technology, telecommunications, or network operations.

1️⃣ Startups using AI to solve real problems — not to run demos

There are two kinds of AI startups in Latin America right now.

Those that built a GPT wrapper and called it a product. And those that identified a concrete industrial problem and used AI to attack it with proprietary data.

The second group is the one worth following.

Bruna is a Chilean startup that uses AI to forecast mineral quality and optimize production plans in mining. In simple terms: instead of a technician manually testing samples to decide whether a batch is ready to process, the model analyzes input data and predicts the outcome before the process starts. Less waste, more consistency, less waiting time.

SearchBrand.ai, incorporated in July 2025, is the region’s first platform that lets brands and SMBs measure and optimize their presence in results generated by AI models such as ChatGPT and Google Gemini. The problem it solves is new but concrete: if someone asks ChatGPT “what is the best dental clinic in Santiago” and your clinic does not appear, you do not exist for that user — just like on Google ten years ago, but without tools to manage it.

The difference between an AI startup that scales and one that dies in the pilot is not the model it uses. It is whether it has access to proprietary data for the problem it is solving.

Sources: Bruna — mining and predictive AI, University of Chile — Madeleine Valderrama, CEO of Bruna AI, SearchBrand.ai — AEO/GEO platform, El Ecosistema Startup — AEO in conversational AI

2️⃣ What Claude does in finance that ChatGPT still does not

On the Chatbot Arena leaderboard for May 2026, GPT-5.5, Claude Opus 4.7 Thinking, and Gemini 3.1 Pro are separated by a single Elo point. For practical purposes, all three are frontier models. The gap in general capability is marginal.

The real difference is where each one is betting as a platform.

On May 5, Anthropic gathered banks, funds, and asset managers in New York to launch Claude for Financial Services: ten ready-to-use AI agents, Microsoft 365 integration, and governed access to data from Moody’s, FactSet, Morningstar, S&P Global, and others.

The most concrete example from the event: JP Morgan CEO Jamie Dimon built, in twenty minutes from a blank sheet, a live analysis dashboard on Treasury asset swaps and bid-ask spreads. Work that normally takes a team of junior analysts half a day.

Three of the ten agents target equity research directly: one reviews earnings calls and updates financial models automatically, another generates daily briefings on coverage lists, and a third turns target-company data into a valuation Excel file ready for review.

Anthropic is explicit: agents produce drafts for qualified human review. They do not execute trades or write directly into accounting records. But that does not reduce the impact — it cuts professional time from hours to minutes.

Anthropic’s bet is not to replace the analyst. It is to replace the three hours the analyst loses preparing context before they can think.

Sources: Anthropic — agents for financial services, CFO.com — Claude’s expansion across corporate finance, ADSLZone — Chatbot Arena+ May 2026

3️⃣ Network traffic changed shape — and most architectures do not know it yet

For decades, network design had a fixed premise: traffic flows downward. Users request, the network delivers. Little upstream, lots of downstream, with predictable peaks in human hours.

The numbers coming out this year show that is no longer true.

In 2025, automated traffic grew 23.5% year over year — eight times faster than human traffic, which grew just 3.1%. And within that automated traffic, traffic generated by AI agents grew 7,851% in a single year. That is not a typo.

Cloudflare, which processes tens of millions of HTTP requests per second across more than 125 countries, reported that in Q1 2026 nearly one in three requests on the internet is already a bot — and 22% of that bot traffic is AI crawlers, the fastest-growing category.

To put it simply: before, a company downloaded data from the internet and barely sent anything back. Now, that same company’s AI agents are constantly sending, querying, and syncing data across multiple systems at once — no schedules, no predictable peaks, in every direction. Networks were designed for the first pattern, not the second.

The problem is not total network capacity. It is that assumptions about how traffic flows — direction, time of day, pattern — are ceasing to be valid.

What these numbers mean in practice: teams sizing network capacity today from historical human-traffic patterns are working with assumptions that no longer apply. This is not a future problem. It is a configuration problem already in production.

Sources: HUMAN Security — State of AI Traffic 2026, Cloudflare Radar Q1 2026 analysis

4️⃣ 5G has 2.8 billion connections — and operators still do not know how to charge for it

Imagine building an eight-lane highway, charging the same toll as the two-lane road you replaced, and then wondering why you cannot recover the investment. That is essentially what is happening to the sector with 5G.

According to the Ericsson Mobility Report from November 2025, 5G already represents nearly 2.8 billion subscriptions — more than 30% of all mobile connections worldwide — yet connectivity revenue growth has been modest: roughly $1.3 trillion in global connectivity revenue in 2025, up about 4% year over year, far below the 5G infrastructure investment curve of the past five years.

The underlying problem is structural. Only about one quarter of operator groups have deployed 5G standalone at scale — the architecture that actually enables network slicing and ultra-low-latency applications. The rest have 5G in name but still run on 4G logic underneath.

A concrete example: network slicing lets an operator sell a hospital a guaranteed “slice” of the network exclusively for its connected medical devices — with contractually guaranteed latency and availability at a premium price. That is real monetization. Without standalone architecture, it does not exist. It is only marketing.

The exit the market is starting to explore is selling network capacity as AI infrastructure — inference as a service, edge computing, guaranteed connectivity for critical applications. In short: stop selling gigabytes and start selling outcomes.

5G did not fail. The assumption that the same 4G business models would work on a radically different network did.

Sources: Ericsson Mobility Report — 5G subscriptions, GSMA — 5G Standalone full steam ahead, GSMA Intelligence — Global Mobile Trends 2026, Omdia via Telecompaper — connectivity revenue 2025

5️⃣ The security risk AI introduced in telecom — and nobody budgeted for

AI is making networks smarter. At the same time it is creating a category of risk the sector did not have before: autonomous failure at scale.

The clearest case last year was NTT DOCOMO in Japan. On January 2, 2025, a DDoS attack caused network congestion that left about 90 million subscribers struggling to access key services for roughly 12 hours. It was not a sophisticated hack. It was volume — enough traffic to overwhelm automated network management systems that responded incorrectly because the data they were processing no longer reflected the real state of the network.

That is exactly the new risk: AI systems that manage networks can take “confidently incorrect” actions when underlying data is manipulated or noisy, causing massive outages — not because the system fails, but because it acts correctly on wrong information.

The scale of the IoT problem is just as concrete. In November 2025, Cloudflare recorded a 31.4 terabit per second DDoS attack — the largest on record — attributed to the Aisuru/Kimwolf botnet, which compromised consumer and industrial IoT devices worldwide. Meanwhile, connected households faced an average of 29 attack attempts per day in 2025 — nearly triple 2024. The average cost of an IoT security incident is around $330,000; in healthcare environments, it exceeds $7 million. IoT malware attacks grew 124% year over year in 2025.

For those of us in network operations, the analogy is direct: it is the same logic as a poorly tested change taking down production — except now an agent executes the change automatically at 3 AM without anyone approving it.

AI is not inherently insecure. We are automating network decisions at scale before we have the validation controls that autonomy requires.

Sources: NTT DOCOMO — official incident notice, Infosecurity — DDoS against DOCOMO, Cloudflare — record 31.4 Tbps attack, Bitdefender and NETGEAR — connected home 2025, DeXpose — IoT statistics 2025

🧭 Five signals, one common conclusion

The architecture decisions being made today — which data feeds an agent, how the network is sized, whether 5G is a product or infrastructure, what controls exist before automating a change — will define who has real operational capacity in 2028 and who is racing to catch up.

May did not bring a single disruptive announcement. It brought a pattern: AI is no longer a software layer on top of what exists. It is rewriting business models, traffic flows, and risk surfaces at the same time.

Which of these five signals resonates most in your role today? If you have a different read, I would like to hear it in the comments.

✍️ Claudio from ViaMind

“Dare to imagine, create, and transform.”

Also available in Spanish: Tendencias IA & Telecom – Mayo 2026.

🔇 The Silent Crisis in Telecommunications: Networks More Critical Than Ever, but an Industry Under Growing Pressure (2026)

Fri, 15 May 2026 00:00:00 +0000

There was a time when working in telecommunications meant being inside an industry that seemed to grow almost naturally.

More customers. More coverage. More rollouts. More investment. More projects.

Today the feeling is different.

And I am not talking only about Chile.

In conversations with people across the industry, in Latin America and Europe, the same themes keep coming up: less capex, fewer large projects, more operational pressure, restructurings, consolidation, and ever-smaller teams.

Yet at the same time, networks have never been more important.

Traffic keeps rising. Customers consume more data than ever. Streaming keeps growing. The cloud keeps growing. Artificial intelligence is starting to push even more demand onto infrastructure, connectivity, and datacenters. Changes keep happening. Incidents still happen. Networks remain critical.

So an uncomfortable question appears:

what is really happening to the telco industry?

I am not a financial analyst or an economist. But I have spent years close to operations, rollouts, and complex telecom projects in both Latin America and Europe. From that experience, one thing feels clearer and clearer:

the work did not disappear.

What disappeared was the margin.

Chile: more pressure, less room to get it wrong

In Chile, over the last few years, the industry has gone through a complex mix of consolidations, restructurings, and extremely aggressive price competition.

Millicom’s arrival at Telefónica Chile came with a major headcount reduction. According to press reports, Millicom disclosed a reduction of roughly 35% of headcount in Chile after taking control of the operation, equivalent to about 1,130 people, based on staffing reported at the end of 2025. (La Tercera)

That number is not small.

When more than a thousand people leave one telco operator, the impact is not confined to that company. It hits the whole market.

Because many of those professionals have experience in similar areas: operations, networks, rollouts, field services, engineering, OSS, PMO, infrastructure, support, vendors, coordination.

And all of that happens in a context where the general feeling is that there is less large investment, fewer greenfield projects, and less room to absorb all that talent.

At the same time, ClaroVTR has also gone through integration and reorganization after its merger. It is not an isolated case. It is part of a broader trend: consolidate, remove duplication, simplify structures, and chase efficiency.

The problem is that networks do not get simpler because there are fewer people.

Quite the opposite.

Customers expect more speed. More stability. More coverage. More availability. More capacity. Fewer failures. A better experience.

While paying less.

And that is probably one of the industry’s big structural problems.

Cheap broadband has an invisible cost

In Chile today you can already find home fiber plans at very aggressive price points, often around CLP 14,000 or 18,000 per month, depending on promotions, coverage, and operator.

From the consumer’s point of view, that sounds great.

And in part it is.

But operationally, the equation is far more complex.

Because behind that customer there are still field technicians, support, routers, ONTs, backbone, power, monitoring, maintenance, crews, platforms, licenses, call centers, datacenters, and 24/7 operations.

And a single technical complaint with a home visit can wipe out the margin of several months of that service.

The paradox is brutal:

networks are more important to daily life than ever, but the base connectivity service seems to be worth less and less.

And the customer does not see all the complexity behind that operation.

For the end user, the emotional value is often no longer in “having internet.”

It is in Netflix. YouTube. TikTok. Spotify. Gaming. The cloud. AI.

The network became invisible.

Until it fails.

Telcos build the infrastructure, but others capture much of the value

That contrast is probably one of the most interesting things to watch in today’s industry.

Telcos still invest billions in fiber, mobile networks, backbone, datacenters, power, critical operations, and regional infrastructure.

But a large share of digital economic value seems to be captured by platforms that run on top of those same networks.

Netflix, for example, reported annual revenue of roughly USD 45 billion in 2025, with meaningful growth versus the prior year. (Netflix shareholder letter)

And that is not a dig at Netflix.

It is simply a signal of how the market changed.

For years, telcos were the center of the digital economy. Today they often function as the silent infrastructure underneath far more visible, more global, and in many cases more profitable platforms.

That completely changes the pressure on the business.

Because networks are still expensive to build and operate.

Very expensive.

But the market keeps pushing prices down.

The human impact almost nobody talks about

When a large telco cuts hundreds or thousands of positions, the problem does not end at that company.

It also saturates the telco labor market.

Suddenly, many professionals with similar experience are looking for opportunities at the same time.

PMs. Engineers. Network specialists. Operations people. Field teams. Support roles. Vendors. Technical coordinators. Rollout leads.

And if at the same time there is less capex, fewer projects, and less hiring, the result is obvious:

a lot of talent available, but less room to absorb it.

That drives more competition for roles, more uncertainty, more outsourcing, more temporary contracts, and more pressure on the teams that remain.

Because the work still exists.

Networks do not stop because there are fewer people. Incidents still happen. Change windows are still scheduled. Rollouts still move forward. Customers still complain when something fails.

The difference is that fewer people now have to sustain operations that are as complex as before—or more so.

Europe: the same pressure, different scale

What is interesting is that this pressure is not only felt in Chile or Latin America.

In Europe I have seen very similar dynamics.

The difference is scale.

Here the customer may pay more than in Chile, but costs are also much higher: salaries, vendors, power, operations, regulation, datacenters, support, and service levels.

And SLAs are often brutal.

Operational expectations are extremely high. Less tolerance for error. More process. More reporting. More demand on availability, continuity, and response times.

In that context, restructurings, smaller teams, and leaner operating models also show up.

I saw it up close in environments related to Liberty Global, VodafoneZiggo, and Infosys—not as a one-off, but as part of broader pressure: sustain critical infrastructure with less margin, less slack, and more demand.

VodafoneZiggo, for example, announced in 2025 a reduction of 400 positions after losing more than 40,000 customers in the first quarter of that year. (Broadband TV News)

Liberty Global has also continued moving toward a more focused structure and possible spin-offs of subsidiaries, as part of a broader group reorganization. (DatacenterDynamics)

It is basically the same story as in Latin America, with different numbers.

In Chile the pain shows up in aggressive pricing, commercial war, and low margin per customer.

In Europe it shows up in the cost to operate, regulation, market maturity, efficiency pressure, and SLAs.

But the underlying question is the same:

how do you sustain infrastructure that is more and more critical when the economic model squeezes from every side?

So what could the way out look like?

I do not have a closed answer.

And probably nobody has it completely.

The industry is evolving.

But I do not believe telcos can stay stuck selling ever-cheaper connectivity alone.

Because if the business shrinks to competing on price, margin will keep falling.

And if margin keeps falling, pressure on teams, operations, and investment will keep rising.

One possible path is to recapture more value inside the digital infrastructure.

Not only moving bits.

Also offering capabilities closer to the end customer: edge computing, regional datacenters, compute services, critical connectivity, low latency, security, cloud integration, enterprise services, and infrastructure ready for artificial intelligence.

Because at this pace, it does not seem reasonable to assume that all future demand for AI, data, and processing can concentrate in a few central points.

AI will probably live in many places at once.

In large datacenters. In public clouds. In private infrastructure. At the edge. Close to enterprises. Close to users. Close to devices. Close to where data is generated.

And there telcos could have an opportunity.

They have network. They have reach. They have sites. They have infrastructure. They have critical operations. They have physical proximity to the end user.

But capturing that opportunity means moving beyond the traditional role of connectivity provider.

It is not easy.

It takes investment, strategic focus, partnerships, technical talent, real automation, and a more ambitious vision than simply selling more megabits for less.

But maybe that is where part of the answer begins.

An essential industry, but under pressure

I do not think telecommunications is disappearing: AI, the cloud, streaming, and much of the modern economy lean on faster, more resilient networks. The counterweight is financial and operational pressure on those who build and operate them. If the limit is no longer only technical but economic, the open question is whether telcos will reinvent their role in time—or end up trapped in an industry that is more critical every day, but less and less profitable.

Claudio from ViaMind

Dare to imagine, create, and transform.

— Claudio

If any of this resonated, share or comment—that is where the conversation starts.

📡 From VTR Chile to the team in Europe: two sides of the same model

Wed, 06 May 2026 00:00:00 +0000

It is ten in the morning in the Netherlands. A biweekly sync with one market just ended — calendar for the next two weeks, blockers, what they need from us, what we learned in another country that might help them. Thirty minutes later the next one starts, with another market. This one is bigger, more demanding, further behind. They want more testing, more documentation, more explanation for every decision. And they are right to ask: their infrastructure is more complex and the margins for error are smaller.

Meanwhile other projects with other markets run in parallel, plus the usual operational work: upgrades, configurations, patches. All with the same offshore team.

The curious part is that I know this model from the other side. For years I was part of the local team in Chile — the one that receives projects, coordinates internally, and acts as the client of the team in Europe. Today I am in Europe. And that dual perspective changes quite a lot how you understand what actually makes this model work.

1. What the local team does — and why it is not small work

When people talk about offshore models in telecom, the picture that comes to mind is Europe designing everything and the local team executing. That picture is incomplete.

The local team has its own complexity. It owns the datacenter, internal coordination with product, local cables and networks, testing in its own environment. When a project arrives from Europe, it does not only deploy it — it receives it, validates it against reality, spots what does not fit, and escalates. It knows the details that are not in any blueprint: the supplier that takes longer than expected, the legacy system nobody documented well, the regulatory constraint that appeared from nowhere.

Being a good local team means being a good client to the offshore team. Knowing how to ask with context, how to validate with judgment, when to escalate and when to solve alone.

This model does not work because the team in Europe is smarter than the markets. It works when both sides understand they are on the same team.

I remember projects that stalled for weeks not because of technical issues, but because one of the two sides did not see it that way. In the end, both always paid the price.

2. What the team in Europe does — and where the real difficulty lives

Before any instruction reaches a market, the offshore team fought the configurations in the lab, found the problems, fixed them, and built the blueprint the country will receive. It is not magic — it is intense technical work that happens before the local market sees a single line of documentation.

Then comes coordination. Each country has its own calendar, priorities, and pace. Operations are not mixed together — you serve market by market, with focus. But that happens with multiple countries in parallel, at different stages, with different levels of scrutiny.

Some countries barely complain. Some want everything in writing, full traceability, validation meetings at every stage. Both are legitimate internal clients. Learning to read that difference — and responding to each without anyone feeling last on the list — is not in any manual. You learn it with experience and mistakes.

I remember many changes where we tested everything — lab, pre-production, different environments — and still, when we hit production, something happened. Because production is always different. There is pressure, tight windows, many variables moving at once. And on the other side there are customers watching TV, online, using services in real time. It is not a test lab — that is where the money and the company’s reputation sit. Rolling back always has a cost. So the point is not to improvise when something fails, but to plan well and execute the plan. On major changes, the room for improvisation is minimal.

3. When the model crosses the Atlantic — and what gets lost along the way

I worked at VTR when it was part of Liberty Global. At that time, large technology projects arrived designed and tested from Europe. The team in Chile received the blueprint, coordinated local rollout, and benefited from solutions that had already been through more advanced labs, with more resources, with more years of accumulated experience.

There was something genuinely valuable in that model: the Americas received the best of the best, already tested. Someone else, in another market, had already paid for the mistakes.

But there was a limit the model never fully solved. The same set-top box that shipped at scale in Europe — with the costs that implied — was too expensive for the Chilean market. Infrastructure designed for European economies did not always fit local realities. And that gap was not a technical problem. It was structural.

That is probably why regional groups are regional — and not global. Technology can be standardized. Markets and costs cannot.

When I wrapped up my chapter with Liberty in Chile, I was offered to continue from Europe — coordinating the same model for operators such as Ziggo, Virgin Media, Sunrise, or UPC. The role changed; the model did not. But having been on the local team first changed how I understood what markets needed from the hub.

4. What changed with outsourcing — and the tension nobody names

The model I have described so far — internal offshore coordinating markets of the same company — has a variant that has become more common: technology outsourcing, where the team in Europe does not belong to the same group but delivers services under contract.

The difference is not only legal. It is operational and cultural.

In the internal model, measurement tends to be qualitative: service quality, operational continuity, internal client satisfaction. Costs are managed inside one organization and there is room to absorb complexity without it showing up immediately on an invoice.

In the outsourcing model, every hour of work turns into story points, estimated effort, money flowing from one company to another. That brings discipline and visibility — which is not bad. But it also changes what gets optimized. And sometimes what gets optimized is not exactly what the business needs.

The tension is real. And people who run projects in this model live with it every day, even though it rarely shows up in status reports.

5. Why this matters now

The telecom industry in Chile is in motion. Millicom has just entered with Movistar; Claro operates under América Móvil’s regional structure. Pressure toward more efficient models — with specialized teams coordinating multiple markets from one hub — is a logical trend in a sector where margins do not allow duplicating capacity in every country.

I do not know whether that same model will play out at ClaroVTR or Millicom. It will probably be a mix of outsourcing and centralization — telco capex is tight and there is little room for big structural bets. It will be interesting to see how this new trend takes shape in the region.

For my part, I will keep the biweekly meetings — same demands, different calendars, constant prioritization and optimization. It sounds simple on paper. But that is exactly where the complexity of this model in telcos really lives.

Claudio from ViaMind

Dare to imagine, create, and transform.

— Claudio

On either side of the table — what landed with you?

🏗️ AI and datacenters: the new infrastructure boom transforming Latin America

Mon, 04 May 2026 00:00:00 +0000

There is a kind of kickoff meeting that looks straightforward on paper. The client arrives with requirements defined, budget approved, timelines clear. It is all there, in the deck. The team nods. The minutes are signed.

Six months later, that same client calls to say the specifications changed. Not on a whim — because the technology they were going to install went obsolete before the build finished. Now three rooms must be redesigned while the rest of the building is already under construction.

That is not a hypothetical scenario. It is what is happening today in the largest infrastructure projects in the world: datacenters built to support artificial intelligence.

And what no one tells you in the headlines about the billions being invested is what happens after the contract is signed.

1. The wave that has already arrived — and that LATAM is only beginning to feel

Global numbers are hard to wrap your head around. In 2025 the four largest players — Amazon, Microsoft, Google, and Meta — invested roughly $320 billion in AI infrastructure. In datacenter deals alone, S&P Global recorded more than $61 billion in transactions that year, already exceeding everything done in 2024.

But what matters for those of us who work in telecommunications and IT in Latin America is not what happens in Texas or Northern Virginia. It is what is happening in Santiago, São Paulo, Querétaro, and Bogotá.

In March 2026, the Capacity LATAM conference brought operators, investors, and regulators together in São Paulo with a message that would have been unthinkable three years ago: the region is no longer discussed as an emerging market. It is discussed as an execution destination. Brazil and Mexico account for more than 60% of projected expansion. Santiago is positioning itself as a hub for AI-ready campuses, backed by renewable power, regulatory maturity, and links to international submarine routes. The Latin American market is estimated to grow from about $3 billion in 2025 to nearly $7 billion in 2031.

“The question is no longer whether LATAM will be part of the global AI infrastructure. The question is who will have the capacity to execute when the capital arrives.”

That last line is the one that should make us think. Because the capital is already arriving. What is scarce is not money — it is something else.

2. The bottleneck the news rarely mentions

When the media talk about datacenters for AI, they talk about GPUs, gigawatts, billions of dollars. They rarely talk about transformers.

The high-voltage transformers needed to feed a modern datacenter have lead times of 12 to 18 months. Backup generators, switchgear, redundant distribution systems — the same picture. That means if someone decides today to build an AI-ready datacenter in Santiago, critical electrical infrastructure must be ordered before most construction permits are approved.

And permits — which in theory should be streamlined for strategic projects — are getting harder. External reviewers are added, environmental requirements tighten, approvals that used to take weeks now take months.

Then there is cooling. Five years ago, racks handled roughly 5 to 8 kilowatts of density. Today’s racks for AI workloads can demand 15 to 50 kilowatts — in some projects, more than 100 kW per rack. That forces a full redesign of mechanical and electrical architecture, and it makes liquid cooling standard, not exceptional. A liquid cooling system is not installed the same way on every project: configurations vary, distribution unit placement changes, and each client has specific requirements that can shift mid-deployment.

Anyone who has run an IT project in production recognizes that logic immediately: change the requirement, change the architecture, change the plan. What sets a datacenter apart is that scale turns every coordination slip into weeks of delay and millions in rework.

“Capital to build datacenters is plentiful. What is scarce is the ability to deliver when components arrive late, permits get complicated, and the client changes specifications while the project is underway.”

3. What it looks like from the inside — without the ribbon-cutting speech

An AI-ready datacenter in 2026 is not a building. It is an integrated system of subsystems that must work together from day one: power, cooling, networking, physical security, environmental control, and the compute hardware inside.

Building it means coordinating civil, electrical, mechanical, networking, and the client — who defines requirements and, in today’s industry, changes them often. Projects that once needed 750 people on site now move teams of 4,000. Commissioning — validation and go-live — has become as demanding as construction itself, because the margin for operational error is minimal.

Modular, prefabricated design is becoming the standard precisely because it shortens timelines: power skids, cooling assemblies, white-space modules — everything is assembled and tested off-site before installation. But even that takes supplier management and schedule coordination that leaves no room for improvisation.

The challenge is not doing the tasks. It is that tasks have dependencies, and dependencies have dependencies. And when the client changes something — in production, mid-build — the ripple hits the whole tree.

“Building an AI-ready datacenter is not an engineering problem. It is a coordination problem that engineering has to solve.”

4. LATAM: where the real opportunities are — and for whom

Not every opportunity in this market is for those who will pour the concrete for the datacenter. That is the first thing to understand.

The ecosystem around a datacenter is broad: infrastructure design and consulting, local supplier management, systems integration, operations and maintenance, security, connectivity. And in LATAM, where global operators arrive with capital but without local knowledge of the regulatory landscape, service providers, and execution risks — there is real room for those who have that knowledge.

The cities already on the map:

São Paulo — the most mature hub, with the largest installed capacity and the most developed supplier ecosystem in the region. High competition, but also the highest demand.

Santiago — rising fast for three reasons: abundant, relatively cheap renewable power, regulatory stability compared with the rest of the region, and strategic geography with access to Pacific submarine cables. A hub that did not show up on hyperscaler roadmaps three years ago — and does today.

Querétaro (Mexico) — established as Mexico’s main hyperscaler infrastructure hub, with growing investment from AWS, Google, and Microsoft.

Bogotá and Buenos Aires — developing markets with growing local demand but still more regulatory and power-infrastructure friction.

The opportunities are not only for large companies. For professionals in project management, IT infrastructure consulting, or systems integration — demand for talent with real experience on complex projects is growing faster than supply.

5. For those of us who run projects: what to take from all this

It is easy, reading about the datacenter boom, to see it as something that happens to others. To large companies, investors, countries with more capital.

But for people who work in IT project management — in telecom, banking, any industry with critical infrastructure — what is happening with AI datacenters is a direct signal. Not because the next step is building a 300-acre campus. Because the skills needed to execute those projects are exactly the ones that already exist in operational teams: managing complex dependencies, coordinating technical disciplines with different logics, change control in production, communication with stakeholders under pressure.

The difference is scale and industry — not logic.

What the AI infrastructure market is beginning to demand in LATAM is not only datacenter engineers. It is profiles who can manage complexity where requirements change, suppliers have unmanageable lead times, and the client needs delivery certainty in environments that are uncertain by definition.

That profile already exists in the region. Sometimes what is missing is recognition — and positioning.

“The next big infrastructure project will not ask you to change industries. It will ask you to apply what you already know at a scale you have not imagined yet.”

Closing

The headline about billions invested in datacenters is real. What does not appear in that headline is the meeting where someone learned the transformer has an eighteen-month wait, the client changed the cooling spec mid-project, and permits will take twice as long as planned.

That conversation — where real complexity shows up — is what decides whether a project ships. And that is where operational expertise has value that headlines do not measure.

LATAM is on the map. Capital is arriving. The question is who will be ready to execute when the moment comes.

Claudio from ViaMind

Dare to imagine, create, and transform.

— Claudio

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⚡ 5G Has Reached Latin America. The Hard Part Is Next.

Sun, 03 May 2026 00:00:00 +0000

Whenever someone in the industry says that Chile — or Brazil, or Colombia — already has 5G, the conversation often stops there. As if deployment were the end of the story.

It is not. It is only the first chapter.

Latin America has spent years building 5G coverage. Chile was the first in the region to auction spectrum, in 2021. Brazil became a reference for speed. Uruguay moved fast. Active connection counts rise quarter after quarter. And yet the 5G running today in the region still does not unlock the most valuable things this technology can offer.

The question is not whether we have 5G. It is what that network can do — and what it still cannot do.

1. What we have — and what that means in practice

Four operators rolling out 5G in a long, geographically complex country like Chile is an achievement that should not be underestimated. Entel leads national coverage and holds about 40% of active 5G connections. Movistar is present in more than 340 municipalities. WOM completed its rollout after a very bumpy path. Claro pushes in urban areas.

Competition is real, prices are reasonable, and signal reaches places where there used to be nothing useful.

The nuance that rarely makes the headlines: the 5G operating today in the region is almost all NSA (Non-Standalone). The air interface is 5G, yes — but the network core still rides on 4G. That works well for everyday use. But it does not enable the most interesting things 5G can do.

Having 5G coverage and having 5G capabilities are two different things. Latin America has the first. The second is still coming.

2. The next step: Standalone

The move to 5G SA (Standalone) is where 5G starts to be genuinely different. With its own independent core, the network can do things that today simply do not exist in most regional markets.

The most relevant for industry and enterprises is called network slicing: the ability to create virtual lanes inside the same physical network, each with specific guarantees for speed, latency, and security. It is the difference between a public highway and an exclusive lane for critical freight.

In Chile, Entel has said it is exploring 5G SA deployment. In Brazil some operators are further along in trials. But across most of the region, the topic shows up in corporate decks more than in concrete investment plans.

Elsewhere, that conversation is further ahead. At MWC Barcelona 2026, Ericsson and Nokia showed how Standalone networks with embedded AI are changing operations in Europe and Asia — less human intervention, more automatic optimization, services with real guarantees for industry. The destination is a network that behaves like critical infrastructure, not best-effort consumer service.

For Latin America, that leap is not far away in calendar time. It sits in the next investment cycle — if decisions keep pace.

3. Where something interesting is already happening

There is a Chilean case worth knowing because it is not a lab pilot — it is live operations.

The port of Puerto Montt deployed 5G connectivity on Entel and Ericsson infrastructure. On Calbuco Island they reported 418 Mbps down and 240 Mbps up, replacing expensive microwave links that were hard to maintain in fjord areas. The port connects more than seven million annual trips to remote communities — and connectivity became part of how they operate, not an accessory.

In parallel, the salmon industry — Chile is the world’s second-largest exporter — is using 5G for traceability, real-time monitoring, and load control in areas where laying fiber was not viable.

These cases point to something important: the real value of 5G in Chile is not the urban user downloading faster. It is in the sectors that drive the economy and need reliable connectivity in hard places. Ports, mining, aquaculture, logistics. That is where 5G stops being marketing and becomes infrastructure.

3.5 The regional map: who is ahead and who is not

If Chile looks sideways, the picture is telling.

Brazil is the only country in Latin America that launched commercial 5G SA from 2022 — and it is already using it in real industry. Claro and Ericsson deployed a private network at Nestlé’s KitKat plant in São Paulo, connecting autonomous robots and IoT devices with gains of up to 25% in operational efficiency. Gerdau, one of the region’s largest steel producers, also runs on private 5G at its plants. Brazil’s median download speed exceeded 430 Mbps in 2025 — the highest in the region. That is no accident.

Mexico is the counterexample. Without an effective spectrum auction, its operators remain tied to 4G architectures, with no clear roadmap to SA. A huge market, with globally scaled manufacturing, held back by regulatory decisions that never quite land.

Chile made better decisions than Mexico — the auction process was orderly, operators met commitments, and there are already real industrial cases in operation. But Brazil is already one lap ahead. That gap does not close by itself.

4. The gap that does not show up in the reports

The infrastructure arrived. What is missing now is knowing what to do with it.

Markets ahead on 5G Standalone — South Korea, Japan, Germany — did not only roll antennas faster. In parallel they built the ability to use those networks: industrial pilots, alliances between operators and productive sectors, teams that learned to design services on the new infrastructure.

In Chile that process is starting. Puerto Montt and salmon farming are positive signals. There are mining initiatives. There is a 5G SA lab linked to Universidad Católica. But volume and speed are not yet those of a market squeezing full advantage from its head start.

The market picture also does not help acceleration. As we have noted in earlier posts, possible consolidation among operators tends to freeze investment decisions just when the market needs the opposite. It is not catastrophic — but it creates inertia at the worst time.

What is coming in the next few years will not be only more speed. It will be pressure from industries that start demanding network guarantees that do not exist today, and from global vendors selling services that local infrastructure still cannot fully deliver.

Chile has the network. It has the sectors to use it well. It even has cases that prove it works.

What is missing is speed — not in megabits, but in decisions.

To close

5G in Latin America already plays a useful role: better mobile experience, more capacity in dense areas, and B2B cases where high-performance wireless replaces expensive or impossible-to-cable links. That is real and measurable.

The next level — SA, slicing, services with real SLAs — depends on core investment, regulation that keeps up, and operators and enterprises co-designing cases at scale. Until then, having “5G” on the phone is a sign of solid rollout; having “5G as industrial platform” remains a bet in progress.

✍️ Claudio from ViaMind

Dare to imagine, create, and transform.

— Claudio