Changing how you think and operate with AI is the solution to removing its shackles, becoming faster, and freeing yourself to focus on truly human work, unlocking full cognitive Leverage.
The Problem Was Never the AI
Most people do not have bad AI. What they have is constrained AI, artificially limited by the environment in which it is used. The models themselves are already powerful enough to change how we work in meaningful ways, but they are forced into fragmented systems, limited interfaces, disconnected tools, and workflows that repeatedly break continuity. The result is a very familiar disappointment. AI looks impressive in isolated moments, but underdelivers in the actual flow of work, where context and continuity matter much more than a single clever answer.
That disappointment is usually explained as a capability issue. The models are not mature enough yet. The tools are still evolving. The really useful use cases are supposedly still ahead of us. It is a convenient explanation because it places the problem somewhere in the future and outside the organization. If the models get better, if the tools improve a little more, then eventually the promise will catch up with the hype.
I do not think that is what is happening.
The real problem is not the AI. It is everything we put around it. It is the friction between systems, the absence of persistent context, the repeated need to explain the same thing again, the forced switching between tools, and the invisible manual labor of carrying ideas from one place to another. What we often describe as a limitation of AI is, in practice, a limitation of the environment in which it is forced to operate.
That is what I have described elsewhere as the Interface Tax. It is the cumulative overhead created by systems that do not speak to each other, interfaces that interrupt instead of accelerate, and processes that require people to constantly reassemble their own work just to keep moving. Most organizations are still paying that tax without realizing how much of their cognitive energy disappears into it. Some have reduced it. Very few have realized that it can do something even more interesting than disappear.
When the Interface Tax Turns Negative
The intuitive model is simple. If friction slows people down, then removing friction should make them faster. That is true, but it is only the beginning of what actually happens. Once enough friction is removed, the system no longer just becomes more efficient. It starts generating momentum. Work becomes easier not merely because fewer steps are required, but because each step starts strengthening the next.
That is the moment where the Interface Tax turns negative.
Instead of constantly paying for the privilege of getting work done, you start receiving returns. Context carries forward. Drafts become documentation. Documentation becomes structure. Structure becomes execution. Outputs in one place become inputs somewhere else without demanding manual translation. Each step no longer begins from scratch, because the work has continuity. The system starts reinforcing itself.
This is where the whole mental model changes. AI stops feeling like a separate tool you occasionally invoke and starts behaving like a layer across everything you do. It is no longer a special assistant you consult from time to time. It becomes the connective tissue between thought, action, documentation, and organization.
And this is where credibility matters.
None of this means that AI is creating my thinking for me. That is exactly the point I do not want blurred. The value is not that the machine suddenly has something meaningful to say on my behalf. The value is that it dramatically reduces the drag around the things only I can provide. My lived experience as a consultant, my perspective from leading products, my ability to connect longer threads in human behavior, my instinct for where a pattern becomes genuinely interesting, all of that remains the core of the work. AI is not replacing it. AI is removing the filler that used to stand between that thinking and a finished result.
That distinction matters, because there is a huge difference between AI-generated content and human thinking that is AI-enriched. One is a content mill with better branding. The other is a real shift in how intellectual work gets done.
This Website Is the Most Tangible Example I Have
The clearest way to explain what I mean is to describe how the content of this website is actually produced. Not as a productivity tutorial, not as a tool flex, and not as an attempt to convince anyone that a specific stack is magical, but as a practical example of what happens when AI is allowed to work as an integrated assistant instead of a disconnected feature.
Ideas do not always start in the same way. Some weeks, I come in with a very clear perspective that I want to sharpen. I have already noticed a tension, formed an opinion, and want to test whether it can be expanded into something more substantial. Other weeks, I begin with something much more open. I ask for relevant trends, patterns, tensions, or new developments worth thinking about, and then very quickly localize something that connects with my own experience. Usually, that only takes one to three prompts. At some point, something clicks, and the conversation shifts from “what should I write about” to “what have I actually seen that matters here.”
That moment is the one that matters.
Because this is where AI stops being a generator and becomes a counterpart. What we are doing in this chat is not content generation in the lazy sense. It is an interview process. I get pushed to ground abstract ideas in specific examples, to reject weak generalizations, and to connect patterns across work I have done in very different client environments. A trend becomes a lived observation. A lived observation becomes a stronger argument. A stronger argument becomes a structured draft.
That interview process is one of the most important parts of the whole system, because it is where the content becomes mine. AI is useful because it can push, challenge, reframe, organize, and pressure-test. But the substance still depends on whether I can connect the dots in a way that is true to my own work and my own perspective.
That is why I would describe the content on this site as mine, enriched by AI, not written by AI. The combination is the point. I bring the pattern recognition, the judgment, the history, the examples, the stories, and the view on how humans behave in systems. AI brings structure, continuity, pressure, expansion, and executional support. Together, the result becomes much stronger than either side would produce alone.
From Thought to Publishing Without the Old Friction
Once the idea is strong enough and the draft feels right, the process no longer breaks into manual fragments.
Today, content creation happens entirely from within ChatGPT Business on my personal account. That detail matters because privacy matters. My content is not used for model training, which gives me the confidence to let the system operate in a far more integrated way. It also matters because it lets me use collaboration features, such as shared projects, when I want to co-create with other thought leaders, without giving up ownership of the broader setup.
From there, the transition into publishing is no longer a manual exercise. Using a custom WordPress connection to my self-hosted site, the content can move directly from the conversation into a formatted draft in WordPress, including the excerpt. That sounds like a small improvement until you remember how much time used to disappear into the space between “this draft is ready” and “this post is now properly in the CMS.”
That gap used to be one of the biggest time-killers in the whole process. Moving content was the tax. Every iteration increased the chance of losing track of changes, duplicating work, creating inconsistencies, or simply wasting attention on something that added no strategic value whatsoever. The work in that stage was not intellectual. It was clerical.
That is exactly the kind of work that disappears in a better system. Not because I no longer review the result, because I still do. There is still Trust Tax in the loop. I still feel the need to verify the outcome every time. But the review now happens where it belongs: at the level of judgment, not at the level of carrying work from one system into another.
That difference matters more than it sounds. It means the quality threshold is still human, while the administrative drag is no longer human.
The Through-Line Matters More Than the Post
The article itself is not the whole point. This website is not a blog in the casual sense. It is much closer to an open-source consulting engine. That means the post is only one layer of value. Just as important is the ability to maintain a coherent through-line across topics, concepts, categories, and formats over time.
That is where Coda comes in.
I maintain a curated database of ideas, concepts, content topics, post titles, excerpts, adjacent LinkedIn posts, and broader relationships between themes. It functions as the memory system behind the site. It is how I keep track of what has already been written, how topics connect, which conceptual anchors are getting stronger, and where there are still gaps worth exploring. It is not documentation for its own sake. It is an operational knowledge base for the thinking process behind the site.
That used to be another manual burden. Once a post was done, I still had to update the meta-system that gives the site coherence. The content had to be reflected in the database, the inventory had to stay current, and the broader structure had to remain navigable. That is exactly the kind of work people postpone, even when they know it matters, simply because it is one more maintenance task competing with the actual act of creation.
Now the Coda connection takes care of updating that layer automatically as new content is created. And this is where the idea of a negative Interface Tax becomes especially tangible. The content does not just land in WordPress. It also strengthens the memory and management system around the site without asking me to do another round of invisible administrative labor. A finished article automatically improves the clarity, traceability, and usefulness of the broader content engine.
That is not just time saved.
That is a tax return.
Execution Without Bleed
The same principle applies to how I manage the work around the site itself.
I operate in an agile structure personally, not just professionally. That includes content production, broader website development, and the ongoing development of my own thought process. I maintain a backlog of activities and execute them in weekly sprints, with an estimated amount of story points to keep the scope under control. The constraint is deliberate. This work cannot bleed uncontrollably into everything else I do. If it starts behaving like a second full-time job, the whole system becomes unsustainable.
That is where Jira comes in.
The backlog, the sprint planning, and the execution layer all sit inside a workflow that ChatGPT can now help maintain as well. Again, the important point is not that another tool has been added. It is that the organizational layer is being held together by the same assistant that is already present in the creation and documentation layers. A new post is not just a post. It becomes part of a sprint, part of a content system, part of a broader intellectual backlog, and part of a repeatable operating model, all without requiring me to manually orchestrate those transitions each time.
This is one of the places where the whole system becomes more than just convenient. Because the website, the memory system, and the execution layer are no longer separate maintenance burdens, the overall workload does not expand linearly with output. One conversation window can now hold the thinking, the writing, the structuring, the publishing, the documentation, and the sprint implications of a piece of work. There is still review, still correction, still judgment, but there is dramatically less fragmentation.
That is the difference between “using AI sometimes” and actually changing how you operate with it.
The Day Feels Different
The best way to understand this shift is not to look at the output. It is to look at the day.
In a fragmented setup, every meaningful idea comes with hidden labor attached to it. You think of something worth exploring, and immediately the overhead begins. Where do I put this? How do I structure it? Which version belongs where? How do I make sure it ends up on the website, in the content memory system, and in the sprint backlog without losing momentum or creating duplicated work? Every one of those questions costs energy. Not just time, but cognitive freshness.
That hidden cost is what makes many good ideas feel more expensive than they should be.
In a connected setup, the day feels different because the idea no longer arrives with a tax bill attached to it. You capture it, develop it, and the system carries it forward with you. What used to require administrative effort now happens as a consequence of the thinking itself. The article gets drafted. The documentation gets maintained. The organizational layer gets updated. What matters is no longer whether you have the discipline to keep all those systems aligned manually. The alignment is built into the flow.
That changes behavior in very practical ways. You stop postponing good ideas because they seem operationally expensive. You stop losing momentum after the first burst of insight. You stop associating creation with maintenance. And perhaps most importantly, you stop spending your best cognitive energy on the things that should have been system behavior all along.
That is where the real return appears.
The Disappearance of Work
What used to feel like a second job now fits into a few focused hours each week. Not because I became better at doing the same tasks faster, but because I stopped doing most of them.
The overhead disappeared.
The constant switching, the manual formatting, the repeated effort of reorganizing content, the hidden work of keeping multiple systems aligned, the problem of losing track of what was already written, all the invisible labor that consumes time without creating much value is no longer sitting in the middle of the process. It is either gone or absorbed by the system itself.
And what remains is the part that actually matters.
Thinking. Deciding what is worth saying. Connecting ideas. Choosing where to push harder and where to simplify. Developing a point of view. Everything else is handled with far less intervention than before.
This is why the usual language of productivity still feels too small for what is happening. Productivity implies that the work remained the same and only the speed improved. That is not what happened. The shape of the work changed. The unnecessary parts dissolved. The ratio between cognitive work and maintenance work improved dramatically.
That is Cognitive Leverage in practice, using the same tool to both enable and empower cognitive work, exclusive to human beings. Not as a slogan, but as a lived shift in where your mind is allowed to stay.
Why This Feels Different from Automation
It is tempting to call this automation, but that word does not really capture the experience.
Automation still assumes that a recognizable set of tasks exists and needs to be executed more efficiently. It is a useful word for repetitive operational work, but it does not explain what happens when work itself changes texture. What is happening here is more profound than that. Many of the tasks do not simply become faster. They begin to dissolve as separate moments of effort. They become side effects of a system that is structured around continuity.
That is why the whole experience feels different. You are not managing work. You are operating inside a system that manages itself around your intent. And the more that happens, the more your own role moves upward, away from administration and toward judgment.
This is where AI becomes a real assistant rather than a glorified feature. It does not just produce artifacts on request. It carries work across boundaries and keeps it coherent while you stay focused on the part that requires actual cognition.
The Real Constraint
At this point, the obvious question is why this is not the default. If the components already exist, if the models are strong enough, if the systems can already be connected, why are most people still trapped in fragmented workflows where AI feels helpful in isolated moments but underwhelming as a system?
The answer is not technical.
It is structural.
Most environments are not designed to allow this level of continuity. They are designed for separation, for control, for clean boundaries between tools, teams, data sources, and interfaces. That makes them easier to govern in a narrow sense, but it also prevents the emergence of a truly low-friction flow. Even when powerful AI is introduced, it is forced into an environment that constantly interrupts it, limits its context, and prevents it from carrying work naturally from one stage to the next.
The shackles are not part of the AI.
They are part of everything around it.
That is why this article is not really about my setup. My setup is simply the clearest proof I have that the technology is already capable of much more than most environments allow. The point is not that everyone should copy the exact same stack. The point is that when you remove enough friction, the nature of work changes. Not in theory. In practice.
Changing How You Work with AI
This is why the solution is not to wait passively for better models, better prompts, or some future generation of AI tools that will magically fix the problem for us. The more meaningful shift is to change how you operate with the tools that already exist.
That does not mean learning obscure prompt tricks or obsessing over the newest feature release. It means removing friction. Letting context persist. Allowing systems to connect. Trusting continuity enough to stop manually reassembling what the environment should already be carrying for you.
This requires a very different mindset. One that sees AI not as a destination, not as a toy, and not even primarily as a tool, but as a layer that sits across your work and reduces the gap between intention and execution.
Once that mindset changes, the rest of the system starts to look different as well. You stop asking what the AI can do in isolation and start asking what your environment keeps forcing it to stop doing.
The Question That Matters
Most people are trying to get more out of AI. More output. More speed. More capability. Those are understandable goals, but they are still built around the wrong frame.
The better question is this: what would change if you stopped holding AI back?
If the honest answer is “not much,” then the limitation may actually be real. But if the answer is “almost everything,” then the limitation is probably artificial. In that case, the real opportunity is not to push the models harder. It is to remove what stands in their way.
That is not a technical project alone. It is a workflow project, an interface project, a systems project, and increasingly a strategic one.
The Real Outcome
In the end, this is not really about tools, workflows, or even AI itself.
It is about attention.
Where it goes, how it is used, and how much of it gets consumed by things that do not require it.
When the layers between thinking and execution begin to disappear, attention returns to where it belongs. Not on managing work, not on organizing fragments, and not on manually carrying context across boundaries that should not exist in the first place.
It returns to creating value.
And that is where the real leverage is.
Because once you get there, the question is no longer how much you can do.
It is how much you can think.
