For the past few years, almost every conversation around artificial intelligence has revolved around one word: speed.

Faster models. More capable agents. Lower costs. More automation.

Capabilities that surprised us only a year ago are already becoming standard features in the software we use every day. Companies naturally don’t want to be left behind. Everyone is finding a way to bring AI into the business — customer service, marketing, software development, operations, finance and beyond.

But I believe a different question is becoming much more important:

As AI develops this quickly, is our ability to manage it developing at the same speed?

That, to me, is where the real conversation begins.

Can We Really Slow AI Down?

 

There is a growing debate around whether the development of advanced AI should move at a more controlled pace.

I understand the argument.

Because this is not simply a discussion about whether artificial intelligence is good or bad. It is about the growing gap between the speed at which technology develops and our ability to understand it, control it and position it correctly inside our organizations.

But I look at the issue a little differently.

I’m not convinced we can simply slow AI down.

This race is no longer happening between a handful of technology companies.

The United States is in it. China is in it. Europe is in it. Big Tech, startups, universities, open-source communities and billions of dollars of capital are all pushing forward at the same time.

If one company — or even one country — decides to slow down, that doesn’t mean everyone else will hit the brakes too.

So I don’t think the most important question for the coming years is:

“How do we slow AI down?”

I think the better question is:

“How do we build management systems that can move as fast as AI does?”

Because very soon, our biggest challenge may not be what artificial intelligence is capable of doing.

It may be what we allow it to do.

AI Governance Is Becoming a CEO Issue

 

Today, employees across thousands of companies are already using ChatGPT, Claude, Gemini and dozens of other AI tools.

But ask the management teams of those same companies a few simple questions and the answers are not always clear.

Which AI systems are being used inside the company? What company or customer data is being shared with them? Where does AI only make recommendations, and where is it allowed to take action? If an AI system makes the wrong decision, who notices?

And perhaps most importantly:

Where does AI’s authority begin — and where does it end?

Today, these may sound like technology questions.

I believe they will very soon become CEO questions.

Because AI is no longer simply a screen that gives us answers.

It creates reports. It writes code. It responds to customers. It builds campaigns. It updates CRM systems. It analyzes data. It triggers other systems.

The next stage is already becoming visible.

AI will adjust budgets, execute purchases, make operational decisions and assign tasks to other AI agents.

That is where the real shift begins.

When AI moves from answering questions to taking action, it stops being just another tool. It becomes part of the organization.

Intelligence. Action. Accountability.

 

This is also one of the subjects I think about most while building AIVAULT.

I believe the cost of building AI-powered software and creating agents will continue to fall dramatically.

Small teams will increasingly be able to accomplish what once required much larger organizations.

As a result, simply being an “AI-powered company” will no longer be a meaningful competitive advantage.

Everyone will use AI.

The real difference will come from who can integrate AI into real business processes in the right way.

I describe this through three words:

Intelligence → Action → Accountability

First, the system needs to understand.

Then, it needs to be able to act.

But without the third layer, the first two don’t mean much to me.

It must be accountable for what it does.

Why did it make that decision? What data did it use? What did it change? What happened as a result? How much revenue did it generate? How much cost did it eliminate? Can the action be reversed if something goes wrong? And ultimately, who has the final authority?

I believe this accountability layer will become one of the most important parts of the AI economy over the next few years.

“How Many AI Agents Do We Have?” Is Probably the Wrong KPI

 

I already see another race beginning inside companies.

“How many agents have we built?”

“How many processes have we automated?”

“What percentage of our employees are using AI?”

I don’t consider any of these a success metric on its own.

For me, the question is much simpler:

What is the business outcome?

Did revenue increase?

Did costs decrease?

Are customers happier?

Did decision-making become faster?

Did human error decrease?

Did employees gain more time for higher-value work?

If we cannot measure these outcomes, we may be using AI — but we haven’t really gone through an AI transformation yet.

We are simply using a new technology.

There is a significant difference between the two.

The Organization Chart Is About to Get a New Layer

 

I also believe organizational structures are going to change.

Today, companies are built around CEOs, CFOs, CMOs, sales, marketing, operations and technology teams.

Tomorrow, behind that visible organization, there will be another layer:

An AI workforce.

Some agents will analyze.

Some will manage operations.

Some will communicate with customers.

Some will continuously monitor company data.

Eventually, some agents will manage other agents.

And the role of humans will gradually shift from execution to orchestration.

This means the best managers of the future will need more than the ability to manage people.

They will need to know how to manage organizations where humans and artificial intelligence work together.

That is a very different management skill.

Speed and Control Are Not Opposites

 

I don’t believe speed and control should be treated as enemies.

Great companies have always managed to grow quickly while building strong financial controls.

They gave sales teams authority while maintaining oversight.

They entered new markets while managing risk.

They moved fast without giving up control.

We will have to do exactly the same with AI.

Building control mechanisms does not mean slowing innovation down.

In fact, it is what allows innovation to scale.

A car doesn’t become slow because it has brakes.

You can drive faster precisely because you know you can stop when necessary.

I believe the same principle applies to artificial intelligence.

The Next Competitive Advantage: Controlled Speed

 

The competitive advantage of the next era will not come from becoming the company that “uses the most AI.”

I believe it will come from something else:

Controlled speed.

We can access the same models.

We can use the same APIs.

We can build similar agents.

And our competitors can do most of those things too.

But they cannot easily replicate our culture, our customer knowledge, the data accumulated over years, our decision-making systems or the way our organizations learn to work with AI.

That is why I believe the real competitive advantage in the AI era is shifting:

From the model to the organization.

The winners will probably not be the companies that give AI the most work.

They will be the companies that give AI the right level of authority, measure what it does and know when to say “stop.”

Maybe we cannot slow artificial intelligence down.

I’m not even sure we should.

But if the speed of technological development consistently exceeds our ability to govern it, at some point we have to stop calling that innovation.

It becomes uncontrolled growth.

So I believe the question CEOs should be asking themselves today is no longer:

“What else can AI do?”

The better question is:

“How much authority are we ready to give AI?”

I believe this will become one of the defining management questions of the next few years.