The Adoption Curve in Real Life (It’s Messier than the Textbooks Say)

You’ve probably seen it happen. A new tool explodes across your social media feeds, your team starts asking questions, and you’re left wondering whether to embrace it or ignore it. Last month’s OpenClaw rollout is the latest reminder of how chaotic technology adoption really is.

Technology adoption curves are depicted as neat, predictable diagrams, a smooth line moving from innovators to early adopters to the early majority and eventually to late adopters.

In textbooks, the curve looks calm. In real life, it feels more like a storm.

Watching the recent surge of interest around OpenClaw, an open-source AI automation tool that lets developers and non-developers build custom autonomous agents, highlights this contrast clearly.

The tool moved rapidly from Clawdbot to MoltBot to OpenClaw. While its identity was in motion, innovators and early adopters embraced it with enthusiasm. Within days, countless articles and YouTube videos appeared with reviews, tutorials, and predictions about how it would reshape everything.

Within another week, we began hearing a more complete message. People still praised its power, but they also surfaced significant security weaknesses and vulnerabilities that accompany those capabilities.

My goal in this post is less about celebrating OpenClaw itself and more about understanding the real-world adoption pattern that I’ve seen countless times.


Phase 1: The Enthusiasts Light the Fuse

Early adopters jump in first. They’re curious, energetic, and quick to celebrate what they’ve discovered.

They imagine what could be, long before most people fully understand what exists today. They test edge cases, build experiments, share demos, and push boundaries simply because the possibility fascinates them.

This group rarely waits for permission. Their momentum gives a new idea its initial lift.


Phase 2: Quiet Experimenters Emerge

Close behind them comes a second tier of users who watch carefully and learn before speaking.

They begin to explore the tool in private, trying things on their own terms rather than joining the public conversation. Their silence can look like hesitation but usually signals careful attention and research.

They want confidence before committing.


Phase 3: The Tribalization of Opinion

At the same time, people who barely understand the technology start lining up on all sides of the debate as if it were a political issue.

Some declare that it will transform everything. Others warn that it is reckless or dangerous. Still others dismiss it as a passing fad.

Much of this reaction grows from identity, fear, or ideology rather than direct experience. The conversation gets louder while genuine clarity is harder to find.


Phase 4: Rapid Evolution and Ecosystem Growth

If the tool has real potential, the surrounding environment begins to move quickly.

The creators ship frequent updates of their new product. Early adopters invent new uses that nobody predicted. Supporting products (like Cloudflare services or the Mac Mini in the case of OpenClaw’s recent meteoric growth) suddenly see rising demand because they pair well with the new capability. Other companies look for ways to add integrations that make the new tool easier to plug into existing systems.

At this stage, the story shifts from a single product to an emerging ecosystem that amplifies its reach.


Phase 5: The Backlash from the Pioneers

Then a familiar turn arrives.

Some early adopters start getting bored and even a little disillusioned. Others start pointing out limitations, rough edges, and frustrations that were overlooked during their initial excitement. Sometimes they simply move on to the next shiny thing. Other times, sustained use reveals real constraints that only time can expose.

Ironically, the quieter second wave adopters are just beginning to feel comfortable. Enthusiasm and skepticism overlap in the marketplace.


Phase 6: Corporations Hit the Brakes

Meanwhile, large organizations watch from the sidelines while asking serious questions about security, governance, and risk. They focus on oversight, accountability, and long-term stability.

From a leadership perspective, this cautious approach seems safe. They can’t risk the family jewels on a promise of something amazing. At least, not yet.


Phase 7: The Safe Version Arrives

If the capability truly matters and maintains momentum, a major platform provider such as Microsoft, Google, Amazon, (and nowadays) OpenAI, or Anthropic eventually releases something comparable inside their own infrastructure.

This can happen through acquisition, partnership, or independent development. When it does, the risk profile shifts almost overnight.

What once felt experimental and dangerous now feels enterprise-ready. It’s the signal that many CIOs and CISOs were waiting for.


Phase 8: The Irony of Timing

By the time most corporations adopt the new “safer version” of the capability, the original pioneers have already moved on.

They’re chasing the next breakthrough and speaking about the earlier tool as if it belongs to another era. Six months earlier it felt magical. Now it feels ordinary, in part because that earlier innovation did its job of pushing the frontier outward.


What This Means for Leaders

For leaders who care about both capability and security, sprinting toward the bleeding edge rarely makes sense.

Waiting for stability, clear governance, and trusted integration usually serves organizations better. In practice, that means allowing major, “trusted” platforms to bring new capabilities inside their own secure environments before moving at scale.

At the same time, leaders can’t afford to look inward only. Something important is always unfolding beyond the walls of their organization. Entrepreneurs are experimenting. Startups are forming. New approaches and new possibilities are taking shape. If a company becomes too passive or too comfortable, it risks being outpaced rather than protected.

The real leadership challenge is learning to tell the difference between waves that will reshape an industry and those that will fade.

Some signs of staying power are multiple independent developers building on top of a new technology, respected technologists moving beyond flashy demos into real production use cases, and serious enterprise concerns about security and governance being addressed rather than dismissed.

We don’t need to chase every new wave.

The real test is recognizing the waves that matter before they feel safe enough to bring inside our organization.

Photo by Nat on Unsplash – Innovation is easy to see. Truth is harder to judge.     

What If Jarvis Is Available to Each of Us?

One of the best parts of the Iron Man movies is Jarvis, the ever-present AI system that acts as an extension of Tony Stark’s mind. Jarvis is a collaborator. A research analyst. A pattern finder. A problem solver. He handles logistics, runs calculations, surfaces insights, and stays ready in the background until Tony needs him.

Jarvis amplifies and extends Tony’s genius.

Recently, I introduced a friend to ChatGPT. He hasn’t jumped into any AI tools yet, but he can see that people around him are finding real value in them. Like many thoughtful people, his first questions weren’t about features. They were about data privacy. About whether these tools were simply repackaging other people’s work. About what was really going on under the hood.

At one point, he asked a simple question:

Is it like having Jarvis around whenever you need him?

To me, the honest answer is yes.

But it’s also important to realize that Jarvis isn’t perfect. And neither are the AI tools available to us today.

The First Questions Matter. Almost every serious conversation about AI tools begins in the same place.

Is my data safe?

Who owns the output?

Can I trust what I’m getting back?

These are the same questions we ask whenever a new digital tool emerges.

At a basic level, paid versions of tools like ChatGPT don’t use our conversations to train public models. Even with that protection in place, I still guard my data carefully. If I’m asking questions related to finances, health, or legal matters, I use hypothetical scenarios rather than personal specifics. I’m the first line of defense when it comes to my personal information.

In professional and commercial environments, organizations using business or enterprise versions gain additional protections around data isolation, encryption, access controls, and audit logging. At the enterprise level, some platforms even allow customers to manage their own encryption keys on top of the platform’s security.

The tool doesn’t decide what’s appropriate to share. We do.

Who Owns the Output? We do. The tool doesn’t claim authorship. It doesn’t retain ownership of what it produces for you. The output becomes yours because you directed the work, supplied the context, and decided how the result would be used.

But ownership is only part of the story. Responsibility matters just as much.

The tool doesn’t know your intent. It doesn’t understand your audience. And it doesn’t bear the consequences of getting something wrong. That responsibility stays with the human in the loop. That’s us.

In that sense, using AI isn’t fundamentally different from working with many other analytical tools we may have used for decades. The work becomes yours because you shape it, refine it, and ultimately stand behind it.

A Note on Sources and Attribution. Owning the output also means owning the responsibility for its accuracy and integrity. This is especially important when it comes to research and citations.

AI tools can pull together large volumes of information, synthesize ideas across many inputs, and present them in clean, compelling language. That capability is incredibly useful. But it doesn’t remove the author’s responsibility to understand where ideas come from and how they’re represented.

The tool may summarize research. It may surface commonly known concepts. It may produce language that sounds authoritative and polished. What it doesn’t guarantee is proper attribution or assurance that content isn’t too closely mirroring a specific source.

That responsibility stays with the human.

When I use AI for research or writing, I treat it as a starting point. I ask it to surface each source. I follow links. I read original material. And when an idea, quote, or framework belongs to someone else, I make sure it’s credited appropriately. This step also helps catch hallucinations that sound amazingly accurate.

Ownership requires standing behind the integrity of the work to the best of your ability.

Can I Trust What I’m Getting Back? Usually. Only with supervision. AI tools are very good at consuming information, identifying patterns, and accelerating first drafts. They are less reliable when precision, nuance, or real-world verification is required.

They can be confidently wrong. They can lose context. They can blend accurate information with outdated or incomplete details.

AI tools hallucinate regularly, though this tendency improves with each new model release. These aren’t reasons to dismiss AI as a tool. They’re reminders to understand what AI is and what it isn’t.

Trust paired with skepticism is the right approach. AI tools are fast-thinking assistants, never the final authority.

Verification still matters. Judgment still matters. Experience still matters. In fact, the better your judgment, the more valuable these tools become.

Why Memory Changes the Equation. Most people use AI tools like a smart search engine. Ask a question. Get an answer. Move on.

That works. But it barely scratches the surface of what’s possible. The real multiplier happens when the tool is allowed to remember context.

ChatGPT includes a memory capability that lets you intentionally store preferences, patterns, and reference material across conversations. Used well, this transforms the tool from something you query into something you can collaborate with.

Over the past year and across hundreds of prompt conversations, I’ve shared:

-My writing voice and stylistic preferences

-A digital copy of a leadership book I wrote over a decade ago (about 65,000 words)

-An autobiography I wrote for my children and grandchildren (about 90,000 words)

-Hundreds of blog posts published over the past 13 years (roughly 240,000 words)

-How I like to structure projects and approach new work

In total, I’ve trained the tool with nearly 400,000 words of my original content. This began as an experiment to see if I could reduce generic responses and encourage the tool to approach questions from my foundational perspective.

The difference is tangible. Early on, whether I was drafting communication, analyzing problems, or organizing ideas, the tool would produce polished but generic output that required extensive rewriting. Now, it reflects my priorities, uses frameworks I’ve shared, and produces work that feels aligned with how I think. I still edit quite a bit, but I’m refining rather than rebuilding.

Collaboration Requires Judgment. My friend asked me another important question.

Do you still feel like the writing you produce with it is yours?

Yes. Completely.

Every project I’ve worked on with these tools begins with my original content, reinforced by reference material I created long before AI entered the picture. Hundreds of thousands of words written over more than a decade. Clear intent about audience and purpose, using a defined process I’ve established before drafting anything.

The tool supports rather than replaces my judgment. Drafts usually require significant edits, shifts in tone, and sometimes complete rewrites.

Where it excels is in synthesis. In retrieval. In pattern recognition across large bodies of work. In accelerating first drafts that already have direction.

Large projects require constant supervision. Threads get crossed. Context gets muddled. The tool needs redirection, clarification, and sometimes retraining as the work evolves.

This is simply the nature of collaboration.

Why the Hype Misses the Point. There’s a popular narrative circulating that anyone can now write a book, write a complex software application, create a website, start a business, or become an expert with just a few well-written prompts.

This misunderstands both the tools and the craft associated with each of these tasks.

I think of AI the way I think of a great camera. We can all buy the same equipment. That doesn’t guarantee an amazing photo. The quality still depends on the eye behind the lens, the patience and skills to frame the shot, and the willingness to edit ruthlessly afterward.

Ansel Adams once said that asking him what camera he used was like asking a writer what typewriter he used. The tool matters. But it has never been the point.

The same is true with AI tools.

Without intent, taste, and care, straight AI output feels flat and formulaic. Readers will notice. Substance can’t be faked. Depth doesn’t appear by accident.

These tools reflect the discipline of the person using them.

Hitting the Ground Running. For someone just getting started, the biggest mistake is expecting magic. The better approach is to build understanding and training into the process (for you and the AI tool).

Explain what you’re trying to do.

Tell the tool how you think.

Correct it when it’s wrong.

Guide it when it drifts.

Treat it like a junior collaborator. One that’s fast, tireless, and remarkably capable…but still dependent on direction and context.

If you’re looking for a practical first step, try this. Find an article you’ve read recently and ask the tool to summarize it. Compare that summary to the original. Notice what it captured, what it missed, and what it misunderstood. This simple exercise reveals both the tool’s strengths and its limitations in a low-stakes way.

From there, you might ask it to help you draft an email, outline a presentation, or brainstorm solutions to a problem you’re facing. Start with tasks where you can easily evaluate the quality of the output and provide feedback on what the tool provides. 

Over time, you’ll notice the quality improves. That’s when the tool begins to resemble the Jarvis we imagined. It isn’t perfect, but it becomes more aligned with what you value most and how you like to approach your work. At the same time, your understanding of its strengths and limitations becomes clearer through consistent use.

AI doesn’t replace thinking. It requires it.

Used carelessly, it produces noise. Used deliberately, it sharpens your insights.

The question is whether we’re willing to slow down at the beginning, set expectations, and engage AI tools with proper intention.

Only then can these tools truly serve us well.

Photo by Chris Haws on Unsplash – photographers often say, “It’s about the photographer, not the camera.”

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Measuring the AI Dividend

In the early 1990s, the term Peace Dividend appeared in headlines and boardrooms. The Cold War had ended, and nations began asking what they might gain by redirecting the resources once committed to defense.

Today the conflict is between our old ways of working and the new reality AI brings. After denial (it’s just a fad), anger (it’s taking our jobs), withdrawal (I’ll wait this one out), and finally acceptance (maybe I should learn how to use AI tools), the picture is clear. AI is here, and it’s reshaping how we think, learn, and work.

Which leads to the natural question. What is our AI Dividend?

Leaders everywhere are trying to measure it. Some ask how many people they can eliminate. Others ask how much more their existing teams can achieve. The real opportunity sits between these two questions.

Few leaders look at this across the right horizon. Every major technological shift starts out loud, then settles into a steady climb toward real value. AI will follow that same pattern.

The early dividends won’t show up on a budget line. They’ll show up in the work. Faster learning inside teams. More accurate decisions. More experiments completed in a week instead of a quarter.

When small gains compound, momentum builds. Work speeds up. Confidence rises. People will begin treating AI as a partner in thinking, not merely a shortcut for output.

At that point the important questions show themselves. Are ideas moving to action faster? Are we correcting less and creating more? Are our teams becoming more curious, more capable, and more energized?

The most valuable AI Dividend is actually the Human Dividend. As machines handle the mechanical, people reclaim their time and attention for creative work, deeper customer relationships, and more purpose-filled contributions. This dividend can’t be measured only in savings or productivity. It will be seen in what people build when they have room to imagine again.

In the years ahead, leaders who measure wisely will look beyond immediate cost savings and focus on what their organizations can create that couldn’t have existed before.

Photo by C Bischoff on Unsplash – because some of the time we gain from using AI will free us up to work on non-AI pursuits.