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.
New platforms arrive. Old tools fade. Processes are reworked. Skills must evolve.
In that sense, disruption has long been part of the job description.
Software developers create new and improved tools. They streamline workflows. They automate tasks that once required entire teams. Over time, they have reshaped and disrupted how work gets done across nearly every industry.
This pattern has been in place for decades.
For software developers, something different is happening now.
With the arrival of AI-assisted development tools, including systems like Anthropic’s Claude Code, disruption has begun to turn inward. These tools are reshaping how developers approach their own work.
For many in the profession, this feels unfamiliar.
Software development continues, but the definition and details of the role are shifting. Tasks that once required sustained manual effort can now be generated, refactored, tested, and explained with remarkable speed.
A developer who once spent an afternoon writing API integration code might now spend fifteen minutes directing an AI to produce it, followed by an hour reviewing edge cases and security implications. The center of gravity moves toward judgment and direction rather than execution and production.
When job roles experience disruption, responses tend to follow predictable patterns. Some people dismiss the change as temporary or overhyped. Others push back, trying to protect familiar and comfortable ways of working. Still others approach the change with curiosity and engagement, interested in how new capabilities can expand what’s possible.
Intent Makes the Difference
An important distinction often gets overlooked when discussing pushbacks.
Some resistance grows from denial. It spends energy cataloging flaws, defending established workflows, or hoping new tools disappear. That approach drains effort without shaping new outcomes. It preserves little and teaches even less.
Other forms of resistance grow from professional judgment.
Experienced developers often notice risks that early enthusiasm misses. Fragile abstractions, security gaps, maintenance burdens, and failures that appear only at scale become visible through lived experience. When developers raise concerns in the service of quality, safety, and long-term viability, their input strengthens the eventual solution. This kind of resistance shapes progress rather than attempting to stop it.
The most effective developers recognize this shift and respond deliberately. They move away from opposing new tools and toward advocating for their effective use. They ask better questions. They redesign workflows. They establish guardrails. They apply experience where judgment continues to matter.
In doing so, they follow the same guidance developers have offered others for years.
Embrace new tools. Continually re-engineer how work gets done. Move upstream toward problem framing, system design, and decision-making.
Greater Emphasis on Judgment
AI generates code with increasing competence. Decisions about what should be built, which tradeoffs make sense, and how systems must evolve over time still require human judgment. As automation accelerates, these responsibilities grow more visible and more critical.
This opportunity in front of developers calls for leadership.
Developers who work fluently with these tools, guide their thoughtful adoption, and help their teams and organizations navigate the transition become trusted guides through change. Their leadership shows up in practical ways:
-pairing new capabilities with healthy skepticism
-putting review processes in place to catch subtle errors
-mentoring junior developers in how to evaluate results rather than simply generating them
-exercising judgment to prioritize tasks that benefit most from automation
Disruption has always been part of the work.
The open question is whether we meet disruption as participants, or step forward as guides.
We call it Artificial Intelligence, but large language models don’t think, reason, or understand in human terms.
A more accurate description might be Artificial Idea Iteration since these tools dramatically compress the cycles of research, drafting, testing, and revision.
SpaceX didn’t transform spaceflight by having perfect ideas. They collapsed the time between ideas and reality. Failing fast, learning quickly, and iterating relentlessly.
AI creates the same dynamic for knowledge work, letting us move from intuition to articulation to revision in hours instead of weeks.
Engineers rely on wind tunnels to test aircraft designs before committing real materials and lives. AI does this for thinking.
Iteration itself isn’t new. What’s new is the scale for iteration that we now have at our fingertips. We can explore multiple paths, abandon weak directions quickly, and refine promising ones without the time, coordination, and risk that once kept ideas locked in our heads.
When iteration becomes inexpensive, we can take more intellectual risks and shift from trying to always be right to trying to always get better.
It’s ironic that as iteration is becoming cheaper and faster with AI tools, human judgment becomes more valuable. Someone still needs to know what’s worth developing, what deserves refinement, and when something is complete rather than exhausted.
The intelligence was never in the machine. AI simply gives us the capacity to develop ideas, test them against reality, and learn from the results at a scale and speed we’ve never had before.
Photo by SpaceX on Unsplash – when SpaceX proposed the idea of landing and reusing their rocket boosters after each launch, the idea seemed impossible. Now it’s happening about 3 times per week…and they’re just getting started.
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.
There’s a quiet moment in meaningful work when your idea begins to live in someone else. You see it in the way they talk about it. You hear it in their enthusiasm. You notice how they add their experience and their language to it until the idea carries their imprint as much as yours.
It can feel strange the first time it happens. You know the origin, but they suddenly feel the spark of the idea for themselves. That’s the moment you know your idea has begun to grow.
Real success often arrives like this, but we don’t always notice it. People begin to adopt your idea, reshape it, and eventually believe in it with a conviction that can be surprising. They explain it to others in their own voice. They defend it. They improve it. If the idea spreads far enough, some will forget where it began. Your name may fade from the origin story. That loss of attribution can sting if you hold the idea too tightly. It should feel like success instead.
Leaders have a responsibility here. Ideas rarely spread through logic alone. They spread through emotional ownership that grows when people discover a piece of themselves in the idea. When that happens, they carry the idea farther than you ever could by insisting on authorship.
A leader’s task is to create the conditions for this transfer. You offer the early shape of the idea, then invite others to step inside and help build the next version. You ask for their insight, their experience, and their concerns. You let their fingerprints gather on the surface until the idea becomes a shared creation. People support what they help to shape.
As others begin to adopt your idea, they’ll need to feel safety in their new enthusiasm. They need to know they’re not the only ones who believe in this direction. A wise leader pays attention to this. They take the people who have embraced their idea and introduce them to others who have done the same. They form new connections, helping to create a small community where confidence strengthens and courage grows. When people see others adopting the same idea, they feel validated, understood, and ready to act.
This is how ideas gain momentum inside organizations. One person sees the promise. Another begins to shape it. A third begins to feel inspired. Before long, it becomes a shared narrative. It starts with your imagination, but it continues through their belief and conviction.
Once people begin to adopt your idea, you must release it. You may or may not receive credit for it. Either outcome is acceptable.
The goal was never to build a monument to your creativity. The goal was to move the organization forward. When others bring your idea into new conversations without you, your contribution has done its job.
Your attention can return to the horizon. There’s always another idea waiting for you, another possibility that needs your curiosity, another problem that needs new framing.
Good leaders plant seeds. Great leaders celebrate when those seeds take root across the organization.
Inspired by Dr. Michael Levin’s post, h/t – Tim Ferriss
Photo by Alex Beauchamp on Unsplash – a new idea taking root and growing beyond its beginning.
Imagine two managers sitting at their desks, both using the same AI tool.
The first asks it to write the same weekly report, just faster. Three hours saved. Nothing new learned. Box checked.
The second uses the AI differently. She asks it to analyze six months of data and search for hidden patterns. It reveals that half the metrics everyone tracks have no real connection to success. Two new questions emerge. She rebuilds the entire process from scratch.
Same tool. Different questions. One finds speed. The other finds wisdom.
This is the divide that will define the next decade of work.
For a long time, leadership revolved around structure and repetition. The best organizations built systems that ran like clockwork. Discipline became an art. Efficiency became a mantra.
Books like Good to Great showed how rigorous process could transform good companies into great ones through consistent execution. When competitive advantage came from doing the same thing better and faster than everyone else, process was power.
AI changes this equation entirely. It makes these processes faster, yes, but it also asks a more unsettling question. Why are you doing this at all?
Speed alone means little when the racetrack itself is disappearing.
Curiosity in the age of AI means something specific. It asks “why” when everyone else asks “how.” It uses AI to question assumptions rather than simply execute them. It treats every automated task as an opportunity to rethink the underlying goal. And it accepts the possibility that your job, as you currently do it, might need to change entirely.
That last part is uncomfortable. Many people fear AI will replace them. Paradoxically, the people most at risk are those who refuse to use AI to reimagine their own work. The curious ones are already replacing themselves with something better.
Many organizations speak of innovation, but their true values show in what they celebrate. Do they promote the person who completes fifty tasks efficiently, or the one who eliminates thirty through reinvention? Most choose the first. They reward throughput. They measure activity. They praise the person who worked late rather than the one who made late nights unnecessary.
This worked when efficiency was scarce. Now efficiency can be abundant. AI will handle efficiency. What remains scarce is the imagination to ask what we should be doing instead. Organizations that thrive will use AI to do entirely different things. Things that were impossible or invisible before.
Working with AI requires more than technical skills. The syntax is easy. The prompts are learnable. Connecting AI to our applications isn’t the challenge. The difficulty is our mindset. Having the patience to experiment when you could just execute. The humility to see that the way you’ve always done things may no longer be the best way. The courage to ask “what if” when your entire career has been built on knowing “how to.”
This is why curiosity has become a competitive advantage. The willingness to probe, to question, to let AI reveal what you’ve been missing. Because AI is a mirror. It reflects whatever you bring to it, amplified. Bring efficiency-seeking and get marginal gains. Bring genuine curiosity and discover new possibilities.
Here’s something to try this week. Take your most routine task. The report, the analysis, the update you’ve done a hundred times. Before asking AI to replicate it, ask a different question. What would make this unnecessary? What question should we be asking instead?
You might discover the task still matters. Or you might realize you’ve been generating reports nobody reads, tracking metrics nobody uses, or solving problems that stopped being relevant two years ago.
Efficiency fades. What feels efficient today becomes everyone’s baseline tomorrow. But invention endures. The capacity to see what others miss, to ask what others skip, to build what nobody else imagines yet.
The curious will see opportunity. The creative will see possibility. The courageous will see permission. Together they will build what comes next.
The tools are here. The door is open. Work we haven’t imagined yet waits on the other side. Solving problems not yet seen, creating value in ways that don’t exist today.
Organizational culture, not technology, is the hardest part of innovation
How many of your projects are truly innovative? If you have any, what’s your success rate? Would you consider your success rate to be all-star caliber?
This baseball analogy is almost a cliché, but it holds up. A professional hitter with a .300 average is considered excellent (all-star?). That means they fail seven times out of ten.
Now imagine applying this to innovation. What if only 30% of your projects succeed? At first glance, that sounds like a losing record. But if the successful projects provide 10x productivity increases, transform your customer’s experience, or massively boost profitability…30% success would yield incredible results for your organization.
This is the kind of opportunity in front of us today with AI. Tools are maturing quickly. The potential is staggering. Every company, large or small, is beginning to experiment.
Some will tiptoe. Others will dive headfirst. All will face a mix of breakthroughs and busts.
There will be tools that don’t deliver on promises, pilots that fizzle, and teams that struggle with adoption. But there will also be amazing homeruns. Projects that reshape the business and redefine what’s possible.
Many leaders today are focusing on which AI tools to purchase and how to train their teams. That’s the easy part.
The harder part is creating space for both the hits and the strikeouts. If people feel they must succeed every time, they probably won’t swing at all. They’ll play it safe and stick with what they know.
Innovation will grind to a halt.
Providing room to fail doesn’t mean celebrating mistakes. It means making sure your team knows that experiments, even the ones that fall short, are part of making progress. Leaders who demand perfection get compliance. Leaders who make room for failure get innovation.
As you lead your organization into AI and beyond, remember that your job isn’t to guarantee every swing is a hit.
Your job is building a culture where people are willing to keep taking swings.
Automation makes the machine run smoother. Innovation changes where the machine is going.
Automation hunts for efficiency. It tries to do what we did yesterday, but faster and cheaper. It targets the transactional and trims overhead. It removes steps and reduces friction. When done well, it buys back time.
Automation is valuable work and the price of admission for any organization.
But efficiency alone won’t differentiate.
Innovation asks different questions. Harder questions. Where are we trying to take our customers next? What experience would make them rethink what’s possible with us?
Innovation seeks to create new value.
Innovation needs space, a space that promotes bold and creative thinking.
It might mean dedicating 20% of a team’s work to exploring customer problems without predetermined solutions.
Or creating quarterly “innovation days” where normal metrics don’t apply.
Or creating time in leadership meetings for “what if” conversations instead of only “what’s broken” discussions.
Leaders set the tone. They can focus solely on efficiency, or they can ask questions that point their organization toward innovation.
If your new system creates fewer clicks, fewer steps, and lower costs, you automated.
If you created a new customer journey or opened a new market category, you innovated.
Do both well and you reshape the game.
Automation keeps us strong today. Innovation makes us irreplaceable tomorrow.
Imagine presenting your boss with a blank canvas, expecting them to sketch out the details of your plan—not impressive.
Instead, consider offering a detailed outline of your proposed actions, complete with timelines and expected outcomes. This approach allows your boss to review your thoroughness and provide feedback, while still enabling you to take the lead on the initiative.
As you consistently demonstrate the quality and reliability of your ideas, your boss may rely less on reviewing your plans in detail, knowing they align with your track record of success. This trust opens the door for you to play a more significant role in decision-making and strategy development.
Bosses appreciate having the opportunity to refine and improve upon existing ideas rather than starting from scratch. They usually don’t have the time or are unwilling to take the time to create from scratch. That’s your job.
By presenting well-researched proposals supported by data and evidence, you provide a solid foundation for collaboration. Offering multiple options allows your boss to feel involved in the decision-making process while subtly guiding them toward your preferred solution.
Timing matters. Choose moments when your boss is receptive and avoid times of stress or distraction. By seeking feedback and actively listening to your boss’s input, you demonstrate a willingness to collaborate, adapt, and learn.
Your goal is always to build a relationship of trust and collaboration. When this happens, your ideas have an opportunity to thrive and contribute to your organization’s innovation and growth.
You can walk around with your blank canvas, wondering why your ideas never get attention. Or, you can raise your hand, and share your ideas in a way that multiplies your boss’s (and your) effectiveness. I choose the second option every time.
p/c – Jonny Caspari, Unsplash.com
I have one favor to ask. If you enjoyed this post and found it useful, please forward it to someone else. Thanks!
Grandpa Bob Encouraging Leadership 
You must be logged in to post a comment.