Most people who use AI regularly have developed a pattern that looks like this: something needs doing, they open an AI tool, they work through the task, they close the tool. The interaction is self-contained. The next time something similar needs doing, the process starts from scratch. This is using AI. It's genuinely useful. It saves time on individual tasks and lowers the effort cost of work that used to be heavier. But there's a different mode that produces a different category of result. One where each AI interaction doesn't just complete a task, but contributes something to a system that makes future work easier. We'd call this building with AI, and the distinction matters more than most people realize. ------------- Context ------------- The reactive mode: open tool, do task, close tool, is the default because it matches how we were trained to use software. Software is a tool. You use it to accomplish something specific. When the task is done, the software has done its job. AI can work that way, and often does. But it can also do something tools traditionally couldn't: retain and apply context, build on prior work, and get progressively more useful as more is invested in it. That capability is only realized when interactions are designed to be cumulative rather than isolated. The difference shows up clearly in how two different people might use AI for client work. The first person opens AI for each deliverable, explains the client context, produces the output, and moves on. The second person maintains a structured client brief that gets updated after every engagement: goals, history, communication preferences, past decisions, ongoing context. Every AI interaction starts from that brief. The brief improves over time as more is known. The output quality improves with it. Same tool. Same tasks. Different architecture. And over six months, the second person's client work is significantly faster and more consistent. Not because they learned any tricks, but because they built something that accumulates knowledge rather than resetting it.

There's a narrative about AI and human skills that frames things as a replacement story. AI gets better at X, so X becomes less valuable for humans to develop. There's something to that narrative in certain domains. But it misses a counter-force that's happening at the same time, quietly, in almost every professional context. Some human skills aren't being devalued by AI. They're being amplified. And the most important one: the one that now sits at the center of every AI-assisted workflow, is something most people have never deliberately developed. The ability to give clear direction. ------------- Context ------------- Every AI interaction starts with a human providing input. That input determines the quality of everything that follows. A clear, specific, well-structured brief produces output that requires minimal revision. A vague, incomplete, loosely structured brief produces output that requires significant rework, or that misses the mark entirely and gets scrapped. Before AI, the cost of poor direction-giving was bounded. A vague brief to a colleague produced a back-and-forth that eventually clarified what was needed. The extra time was real but finite, and the human on the receiving end could ask questions, make reasonable assumptions, and draw on shared context to fill gaps. AI can ask clarifying questions, but it can't draw on shared context it hasn't been given. It fills gaps with whatever seems statistically reasonable based on its training, which may or may not match what was actually needed. And unlike a human colleague, it doesn't know what it doesn't know. It produces confident output based on the information available, whether or not that information was sufficient. The result is that the quality of direction-giving is now directly and immediately visible in the quality of output. There's no human buffer to compensate for vague input. The brief is the foundation, and if the foundation is weak, everything built on it is too. ------------- Why Most People Haven't Developed This Skill -------------

You don't need Claude's Fable or Mythos or whatever you think is next, because which model you use doesn't matter as much as you think. It's why Claude was terrified of OpenClaw, but it's too late. We're focusing too much on the model and not enough on the harness around it. Anthropic's own agent guidance talks about prompt chaining, routing, parallelization, evaluator-optimizer loops, and tool design. In other words: the system around the model is doing a most of the important work. Once the harness is strong, the model underneath becomes more swappable. You can use the expensive frontier model when the reasoning really matters, then route simpler work to cheaper models for extraction, classification, etc. OpenRouter Fusion is a signal that this is where companies are going. It runs multiple models in parallel, uses a judge model to compare answers and has a final model use that analysis. That is a loop engineering. Three cheaper models checking each other can be better than one expensive model doing everything alone. Agents move AI beyond generation and reasoning into action, and enterprise software becomes agentic platforms. Satya Nadella recently framed this as a frontier ecosystem, not just a frontier model. Right on! The model is becoming replaceable, but the learning loop is not. The next big open source opportunity isn't whatever comes after Mythos, It's the agent operating system around them

There's a particular kind of productive-feeling procrastination that the AI era has made very easy to fall into. It involves tabs. Usually many tabs. Reviews, comparisons, Reddit threads, YouTube walkthroughs, LinkedIn posts from people using tools you've never heard of. "Is this better than what I'm using? What is everyone else using? Am I falling behind?" An hour passes. No work has been done. But it feels like work, because you're learning about AI. The gap between what you're currently doing and what's theoretically possible feels like a problem you need to solve before you can get back to the actual work. This is one of the most underacknowledged time costs in AI adoption right now, not the failure to use AI, but the consumption of working hours by the pursuit of better AI. ------------- Context ------------- The pace of AI development creates a genuine psychological pressure. New tools are released constantly. Capabilities improve on timescales of months, not years. The thing that was cutting-edge in January can feel ordinary by April. For anyone paying attention, there's a persistent sense that the current setup might be suboptimal, that somewhere out there is a tool or a workflow that would produce meaningfully better results, if only you could identify it. That sense isn't entirely wrong. The tools are genuinely improving. New options really do appear regularly. Some of them are meaningfully better than what came before. But the question worth examining is what the search for those tools is actually costing. Every hour spent researching, evaluating, and switching tools is an hour not spent doing the work that makes a business run. And the time cost of staying current with the AI tool landscape has grown dramatically alongside the number of options available. A straightforward audit shows what this costs at scale. If someone spends ninety minutes per week researching AI tools, reading about new capabilities, watching demos, and evaluating potential switches, that's about 75 hours per year, nearly two full work weeks. Two weeks invested in understanding what's possible. Two weeks not spent executing.

In this video, I show off a prompt we created to make Claude's visuals even more useful. It's a simple trick that you'll definitely want to add to your prompting toolkit. Enjoy! :)