Hello, Futurists!
Welcome to the Monday newsletter, where we go deep into AI topics people often don't want to discuss.
I was recently invited onto a television program and asked a question many people are probably asking themselves:
Why are people afraid of artificial intelligence?
My answer was simple:
“Most people aren't afraid of AI. They're afraid of the change this technology represents.”
AI, by itself, is simply a technology. What really creates fear is thinking about everything that could happen when it starts changing the rules of our lives.
What will happen to my job? Will what I studied still have value? What skills will I need to stay relevant?
That's the real fear. Not fear of AI. Fear of losing stability.
And humans have never had a good relationship with change.
We prefer what we know
We like to think we're rational, but often we're not. We prefer the familiar.
A process can be slow, expensive, and inefficient, but if we understand how it works, we feel comfortable with it.
A new technology might be ten times better, but it also forces us to learn, change habits, and feel like beginners again.
That creates resistance. We see it all the time.
Companies still using old software because replacing it is complicated. People doing tasks manually that could already be automated because they've done them the same way for years. Managers preserving unquestioned processes because “that's how it's always been done.”
We often don't reject a technology because it's bad. We reject it because change is costly.
Technology can already do much more than we're using it for
Imagine AI progress stops completely tomorrow. No new model. No more advanced tool. We have only what exists today.
We'd still have years of work ahead implementing everything these tools can already do.
Today, AI can accelerate or automate a huge number of tasks in areas like:
- Design
- Marketing
- Writing
- Translation
- Research
- Programming
- Administration
- Data analysis
- Customer service
A significant portion of our daily computer work can already be accelerated or partially delegated to AI agents.
That doesn't mean all those jobs disappear tomorrow. This difference is crucial.
A task being automatable doesn't mean the entire job will disappear.
A job usually consists of many tasks. Some are easy to automate. Others need judgment, context, experience, accountability, or human relationships.
And there's another huge barrier: the company has to be willing to change.
Companies move much more slowly than technology
For a company to truly benefit from new technology, paying for ChatGPT isn't enough. It has to change how it operates.
Systems need connecting, processes need reorganizing, people need training, responsibilities need changing, contracts need updating, security rules need creating, legal issues need resolving, budgets need securing, and decision-makers need convincing.
Often, the organization's entire culture needs to change too. All of that takes time.
Sam Altman, OpenAI's CEO, explained something important for understanding our current moment.
After GPT-4 launched, he expected the economy to change much faster. More disruption. More companies rebuilding their products. More people changing how they worked.
But it didn't happen as quickly as he imagined. In an interview, he summed up the problem simply:
“The economy just has an enormous amount of inertia.”
Sam Altman, CEO of OpenAI
People keep using the same tools and buying from the same companies even when much more powerful alternatives exist.
Altman also acknowledged that they've been too ambitious about timelines.
Technology can advance extremely quickly. Society doesn't necessarily do the same. That slowness can be positive because it gives people and the economy time to adapt.
This is where we often confuse two completely different things:
Technological capability and technological adoption.
Something being possible doesn't mean everyone will use it tomorrow.
Real adoption is still very low
The data helps explain this difference.
According to EY's Work Reimagined study, 93% of workers in Latin America already use AI tools, above the global average of 83%.
But here's the interesting part:
Only 28% of organizations are truly prepared to turn that adoption into high-value results.
There's a huge difference between using AI and transforming a company with AI.
Someone can use ChatGPT to improve an email, summarize a document, or create a presentation. That counts as adoption.
But that's very different from a company redesigning sales, customer service, operations, research, or internal processes around agents and automation.
That's precisely the gap EY found. Workers can save hours with AI, but in most companies those individual improvements aren't yet producing major changes in productivity or business results.
For me, that reinforces this article's central idea:
The technology is already here. What's missing is reorganizing companies around it.
That's why we see two completely different realities at once.
In Silicon Valley, everything seems to change weekly. One company launches new agents. Another completely rebuilds its product with AI.
But outside that bubble, thousands of businesses still work almost exactly as they did ten years ago.
Manual emails. Excel sheets. Unnecessary meetings. People copying information between systems. Entire teams doing tasks that could now be automated.
Both realities exist simultaneously. Something similar happens individually.
Millions of people already use ChatGPT and other assistants, but occasional AI use doesn't mean they've really changed how they work.
Asking ChatGPT to improve an email once a week is far from using it to research, analyze information, automate processes, create agents, or integrate it directly into an operation.
Everyone talks about adopting AI. Very few organizations have actually rebuilt how they work around it.
That's exactly where the opportunity is. We aren't waiting for AI to arrive. It's already here.
We're waiting for the economy to figure out what to do with it.
And it's still very early.
That's why layoffs won't all happen at once either
Technology companies will probably be among the first to experience major workforce changes.
That makes sense. They're closer to the technology, their workers understand the tools better, their processes are digital, and there's enormous pressure to adopt quickly.
That's why we're already seeing tech companies shrink their teams while increasing productivity.
But assuming exactly the same thing will happen tomorrow in a restaurant, hospital, construction company, family business, or small business is a mistake.
The economy doesn't move at one speed. Some industries are far ahead. Others will take years. Some will need much longer to change completely.
The history of technology is full of examples like this. Inventing something is only the beginning. Then comes the slow part:
Adapting the world to that invention.
That slowness creates an enormous opportunity
Every new technology goes through two stages. First, it becomes powerful enough. Then, the rest of the world learns to use it.
There's a window between those two points. And we're inside it right now.
That gap doesn't just create an opportunity to work better. It can become a huge business opportunity too.
I recently wrote something that captures how I see this:
Every major technological shift creates two kinds of opportunities. The first is building the new technology. The second, often more accessible, is helping the rest of the world use it.
Not everyone will build the next OpenAI. But millions of companies will need help understanding what they can automate, how to connect data, build agents, change processes, train teams, save time, cut costs, and sell more.
Think of a traditional company receiving hundreds of customer inquiries every week. Someone answers the same questions repeatedly: opening hours, prices, availability, order status, policies, and basic information.
Today, an AI agent can answer many of those immediately and pass only the cases that truly need human attention to a person.
You don't need to imagine AI running the whole company. Sometimes a simple automation can save hundreds of hours a year.
Multiply those savings across sales, support, marketing, administration, operations, and finance, and the impact can be enormous.
The good news is that the technology already exists. The problem isn't always technical.
Often, the business owner doesn't know it's possible. Or knows the technology exists but doesn't know how to implement it. Or is too busy running the business to stop and learn.
That's where the value appears.
This happened in other technological revolutions too.
When the internet arrived, the opportunity wasn't only building browsers. It was also helping companies create their first websites.
When social media arrived, entire agencies emerged to help brands that didn't know how to use it.
We'll probably see the same with AI, but on a much bigger scale.
Consultants. Agencies. Integrators. Automation specialists. People building agents for specific industries. Companies taking old processes and rebuilding them with AI.
Technology may move quickly, but every company needs someone to translate those advances into its own reality.
As long as there's a gap between what AI can do and what companies actually do, closing that gap will be a huge opportunity.
Inertia protects us
We usually see the economy's slowness as negative. In this case, maybe it isn't entirely so.
Imagine every company in the world identified every task it could automate with today's technology and implemented it immediately tomorrow.
Millions of jobs could change almost simultaneously. Universities wouldn't have time to react. Workers wouldn't have time to learn new skills. Governments wouldn't have time to adapt policies. Companies wouldn't have time to discover the new jobs emerging around the technology either.
The shock would be much stronger.
Economic inertia acts like a shock absorber. It buys us time.
Time to learn. Reorganize companies. Discover new jobs. Decide what we want to automate and what we'd rather preserve.
That doesn't mean the transition will be easy. It probably won't. But it could be much more gradual than we imagine when we see an impressive new tool demo.
And that brings us back to fear. People have reasons to feel uncertain. The world really is changing. Many jobs will transform. Some skills will lose value. Denying that would be naive.
But recognizing that change is coming is very different from assuming everything will happen tomorrow.
Technology can advance exponentially. Society doesn't.
Companies have habits. People have fears. Institutions have rules. Economies have inertia.
All of that makes the real world take much longer to change than a technology demo. And, strange as it sounds, maybe that's a good thing.
It gives us time—and an opportunity.
While the rest of the world is still figuring out these tools, you can learn them. Experiment. Build. Become more productive. Even create a business helping others do exactly the same.
Thank you for reading this far.
If you have a minute, send me an email and tell me what you thought of today's piece.