What Becomes Valuable When Content Becomes Infinite?
When Content Was King.
I’ve spent much of my career helping organizations adapt when technology changes the rules. Sometimes that meant rethinking how customers discover information. Sometimes it meant rebuilding the systems, processes, and teams required to compete in a new environment. Looking back, one pattern keeps repeating:
Every technology shift changes what becomes scarce.
The organizations that adapt fastest are usually the ones that recognize where the constraint moved and build new capabilities around it.
AI may be creating one of those moments again.
When content was king
Early in my career at Realtor.com, “content is king” wasn’t just a slogan. It was the operating model. We were reading The Long Tail and taking the idea seriously:
Customer demand didn’t exist only around a handful of large topics. It was distributed across millions of specific questions, needs, and searches.
The companies that could understand that demand and create the best answers at scale had an advantage. So we built systems to do exactly that. I worked on a project that created more than 160 million pages of product-led, data-driven content built around consumer demand. At the time, doing that well was incredibly difficult. Creating relevant content at that scale, connecting it to real inventory, and making it discoverable required technology, data, process, and organizational alignment. The difficulty was the moat. Building the capability was the competitive advantage.
The constraint moved
Years later at Adobe, the challenge looked different. The question was no longer simply:
“How do we create more pages?”
The challenge became:
“How do we unlock expertise trapped inside a global organization and turn it into something valuable for customers?”
The constraint had moved.
We borrowed ideas from places like Theory of Constraints and The 4-Hour Workweek. Find the bottleneck. Build systems around it. Remove unnecessary friction. We broke down the content creation process, supported product experts with writing resources, and created a system that allowed expertise to scale globally. And it worked.
Eventually, we became so effective at increasing content velocity that the next challenge became managing and maintaining everything we had created.
Two different eras. Two different constraints. The same underlying pattern:
Understand what became scarce and build the capability around it.
AI changes the constraint again
For decades, companies invested enormous resources into solving the challenge of creation.
How do we produce more?
How do we move faster?
How do we scale expertise?
Those were the right questions because creation was genuinely constrained.
And this is where the conversation gets complicated. Because the old playbook did not suddenly stop working. In many ways, organizations are just getting better at proving that it works. Today, we can understand customer demand better than ever.
We can connect content strategies directly to business outcomes. We can prove that answering customer questions creates measurable value. And now AI gives us the opportunity to remove many of the constraints that historically slowed us down.
The natural reaction is obvious:
If content works, and AI lets us create more of it...
Create more.
Move faster.
Increase velocity.
And maybe we should.
For many organizations, there is still enormous value trapped behind old constraints. But every major technology shift creates a moment where leaders need to ask whether they are scaling the next advantage or simply scaling the last one. Because when everyone has the ability to create more, the rules of competition start changing. More may still matter. But more alone probably won’t be enough.
What becomes valuable when creation becomes easy?
Every era rewards the capabilities that are hardest to replicate. When information was hard to organize, search created value. When expertise was hard to scale, content systems created value. When creation itself becomes easier, the advantage moves again.
Toward judgment. Not just creating answers, but knowing which answers matter. Toward context. Understanding the customer, the business, and the situation deeply enough to know how an answer should be applied. Toward trust. Because when every company can create polished perspectives instantly, people will increasingly look for signals beyond the content itself.
Who created it?
What have they experienced?
What have they actually learned?
And maybe most importantly:
Toward collective learning.
Because when technology changes this quickly, nobody has the full playbook. The people learning fastest are rarely learning alone.
They are testing ideas.
Comparing notes.
Sharing failures.
Challenging assumptions.
Learning from others trying to solve similar problems.
Why I’m building The Room
Earlier in my career, during another major technology shift, some of my most valuable insights didn’t come from another article, conference deck, or best-practice guide.
They came from conversations with other practitioners navigating the same uncertainty. People willing to share what they were seeing.
What worked.
What didn’t.
What they would do differently.
I believe AI is creating another one of those moments.
There are plenty of AI headlines. Plenty of tools. Plenty of opinions. But there are still not enough trusted spaces where leaders can openly discuss the harder questions:
How should organizations actually change?
What capabilities should we build?
Where is AI creating real advantage?
What are we learning that nobody has written the playbook for yet?
That’s why I started building The Room. A private community for leaders navigating AI, growth, and digital transformation.
Not because anyone has all the answers. Because the next advantage may come from learning faster together.
AI will give every organization more content.
More information.
More answers.
But answers were never the finish line.
The leaders who navigate this next shift will be the ones who understand what changed, and build the capabilities required for what comes next.