Anthropic just released Claude Cowork, the next evolution of Claude built on top of the incredibly effective Claude Code architecture. It essentially gives Claude Code's abilities to everyone, even if they aren't developers or comfortable using a terminal. Watch the video for a full breakdown and testing!

One of the most overlooked barriers to AI adoption is not fear, skepticism, or lack of access. It is overthinking. The habit of analyzing, preparing, and evaluating AI endlessly, while rarely engaging with it in practice. It feels responsible, even intelligent, but over time it quietly stalls learning and erodes confidence. ------------- Context: When Preparation Replaces Progress ------------- In many teams and organizations, AI is talked about constantly. Articles are shared, tools are compared, use cases are debated, and risks are examined from every angle. On the surface, this looks like thoughtful adoption. Underneath, it often masks a deeper hesitation to begin. Overthinking AI is socially acceptable. It sounds prudent to say we are still researching, still learning, still waiting for clarity. There is safety in staying theoretical. As long as AI remains an idea rather than a practice, we are not exposed to mistakes, limitations, or uncertainty. At an individual level, this shows up as consuming content without experimentation. Watching demos instead of trying workflows. Refining prompts in our heads instead of testing them in context. We convince ourselves we are getting ready, when in reality we are standing still. The cost of this pattern is subtle. Nothing breaks. No failure occurs. But learning never fully starts. And without practice, confidence has nowhere to grow. ------------- Insight 1: Thinking Feels Safer Than Acting ------------- Thinking gives us the illusion of control. When we analyze AI from a distance, we remain in familiar territory. We can evaluate risks, compare options, and imagine outcomes without putting ourselves on the line. Using AI, by contrast, introduces exposure. The output might be wrong. The interaction might feel awkward. We might not know how to respond. These moments challenge our sense of competence, especially in environments where expertise is valued. Overthinking becomes a way to protect identity. As long as we are still “learning about AI,” we cannot be judged on how well we use it. The problem is that this protection comes at a price. We trade short-term comfort for long-term capability.

One of the quiet myths around AI adoption is that success comes from staying firmly in control. That if we just give the right instructions, apply enough structure, and reduce uncertainty, AI will behave exactly as we want. In reality, the opposite is often true. The biggest breakthroughs with AI tend to happen not when we tighten control, but when we learn how to collaborate. ------------- Context: Why Control Feels So Important ------------- Most of us were trained in environments where competence was measured by precision. Clear plans, predictable outputs, and repeatable processes were signs of professionalism. Control was not just a preference, it was part of our identity. If we could define every step and anticipate every outcome, we were doing our job well. AI disrupts this deeply ingrained model. It does not behave like traditional software. It responds probabilistically, offers interpretations rather than guarantees, and sometimes produces outputs that are surprising, imperfect, or simply different than expected. For many people, this creates discomfort before it creates value. That discomfort often shows up as over-structuring. We try to lock AI into rigid instructions. We aim for the perfect prompt. We narrow the interaction so tightly that there is no room for exploration. On the surface, this looks like responsible use. Underneath, it is often an attempt to preserve a sense of control in unfamiliar territory. The challenge is that excessive control quietly limits what AI can contribute. It turns a potentially collaborative system into a transactional one. We ask, it answers, and the interaction ends. What we lose in that exchange is insight, perspective, and the chance to think differently than we would on our own. ------------- Insight 1: Control Is Often a Comfort Strategy ------------- When we encounter uncertainty, control feels stabilizing. It gives us the sense that we are managing risk and protecting quality. With AI, this instinct is understandable. We worry about errors, misalignment, or appearing unskilled if the output is not perfect.

The fear of falling behind often feels like a warning, but in reality, it behaves more like a trap. It creates urgency without direction, pressure without clarity, and motion without meaning. When it comes to AI adoption, this myth does not accelerate progress. It quietly undermines confidence, judgment, and long-term capability. ------------- Context: Where the Fear Comes From ------------- We are surrounded by narratives that frame AI as a race. New tools launch weekly, headlines highlight exponential change, and social feeds reward those who appear early, fast, and fluent. In that environment, it becomes easy to believe that progress is measured by speed alone, and that hesitation equals failure. Inside organizations and teams, this fear often shows up subtly. People experiment with tools without a clear reason, adopt workflows they do not fully understand, or push themselves to “keep up” even when the value is unclear. The pressure is rarely explicit, but it is constant, and it shapes behavior more than we realize. At a personal level, the myth of falling behind turns learning into a performance. Instead of curiosity, we feel comparison. Instead of exploration, we feel evaluation. The question shifts from “What would help me think better?” to “What should I already know by now?” That shift is small, but its impact is enormous. Over time, this mindset erodes trust in our own ability to learn. We begin to see AI as something we must catch rather than something we can shape. Adoption becomes reactive, fragmented, and emotionally exhausting. ------------- Insight 1: Falling Behind Is a Story, Not a Fact ------------- The idea that everyone else is ahead is rarely grounded in reality. What we usually see are fragments. A polished output, a confident post, a shared success. What we do not see are the missteps, the discarded experiments, or the long periods of uncertainty that precede real competence. AI capability does not move in a straight line. It develops unevenly, shaped by context, intent, and repetition. Someone may appear advanced because they use a specific tool fluently, while lacking clarity in how it actually supports their thinking or decisions. Another person may move slower, but build deeper judgment and adaptability over time.

We often think confidence means knowing the answer. But in the age of AI, confidence is becoming something else entirely. It is no longer about certainty, but about curiosity, the willingness to explore, learn, and adapt faster than the world changes. ------------- The Changing Shape of Confidence ------------- Many of us built our professional identities around expertise. We were rewarded for knowing, not for asking. Yet AI has begun to erode the traditional link between confidence and knowledge. When information is instantly accessible, knowing more is no longer the edge it once was. Instead, our value shifts toward interpretation, discernment, and creative decision-making. This shift can feel destabilizing. For a designer, it might mean learning to trust AI tools that propose options faster than the human eye can follow. For a manager, it might mean relying on AI-generated insights to guide decisions that once relied on experience alone. The feeling of not “knowing enough” becomes constant. Confidence, in this context, can no longer rest on mastery of fixed skills. It must rest on the deeper trust that we can learn continuously and apply judgment wisely. The professionals thriving with AI are not those who know the most, but those who stay open the longest. In many ways, AI has brought us back to a more human kind of confidence, one rooted in adaptability, curiosity, and collaboration rather than certainty. This is an opportunity, but also a challenge to how we define expertise, leadership, and competence itself. ------------- From Knowing to Navigating ------------- When we ask AI a question, we are engaging in a process of navigation, not of recall. We frame a query, interpret a response, and iterate. The skill lies not in the data we already possess, but in how well we can steer the conversation. This is a profound change in how we work. The most valuable professionals will increasingly be those who can “navigate uncertainty” rather than “store knowledge.” A data analyst, for example, may rely on AI to generate models or visualizations, but must still decide which questions matter and which outputs can be trusted.