Almost Timely News: 🗞️ Demonstrating the Art of the Possible in AI

How to get to wow

Jan 18, 2026

Almost Timely News: 🗞️ Demonstrating the Art of the Possible in AI (2026-01-18) :: View in Browser

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What’s On My Mind: Demonstrating the Art of the Possible in AI

“It’s not who you are underneath, it’s what you do that defines you.” - Batman

One of the most common refrains I hear on a weekly basis is that you feel overwhelmed by the pace of change with AI. Even OpenAI cofounder Andrej Karpathy was recently posting about how far behind he was in terms of what capabilities are now available. If one of the leading minds in the field feels overwhelmed, it’s no wonder the rest of us sometimes struggle to keep up.

So this week, let’s talk about changing focus so you can feel less overwhelmed AND get more things done.

I’ve shared in the past - and we have an entire course about - the seven use case categories of generative AI: extraction, classification, summarization, rewriting, synthesis, question answering, and generation. Those are still important and valid. But here’s the thing: those are process-based categories. They describe how AI works, not what you get out of it.

And each use case category has outcomes, but those outcomes aren’t necessarily the finished products that make people go “wow.” Showing someone a data extract? That’s not really a wow, right? They’re like, “Okay, that’s cool, you got data out of data, but what do I do with it?” It’s one step in a recipe, not the finished dish. You’re not doing just an extraction task in isolation - you’re doing an extraction task that leads to something else, something meaningful.

So I want to talk about how we get to those finished products. How do we get to the wow? How do we get to the art of the possible? What are the outcomes that make people sit up and pay attention?

Think about what outcomes you want with AI, what the tools can do. I’m intentionally ignoring the bigger picture around people and process because that’s what my cofounder and CEO Katie Robbert focuses on. Broadly, our outcomes are almost always about either saving resources (time, money, effort, materials) or creating resources.

When I do live demos at conferences and workshops, I make it look like a magic show. There’s a lot of prep work to make sure I have good prompts and good systems in place, but once those systems are ready, it looks easy. The outputs are pretty terrific, and people can see that it doesn’t take long to do. That’s what creates the wow - watching something happen that they didn’t think was possible.

I’ve developed a framework for thinking about AI outcomes that I call CRAFT: Create, Refine, Arrange, Flip, and Tackle. Let me walk you through each one with examples of what “wow” looks like.

LetterCategoryDefinitionCCreateBuild net new deliverables from ideasRRefineExtend, improve, and modernize existing workAArrangeOrganize chaos, strategize, bring orderFFlipTransform content across domainsTTackleTake on tasks and solve real problems

Part 1: Create - Building What Didn’t Exist Before

Create is about making stuff that simply didn’t exist before. Bringing ideas to life. My number one use case outcome here? Software.

I’m talking about building things that are outside your skill set. I use tools like Suno for music generation because I have zero musical capabilities. Zero. I wrote a trashy romance novel a few weeks back in my newsletter because it illustrated AI’s capabilities perfectly - I can take an idea and bring that idea to life. That’s the heart and soul of Create.

If you’ve got an idea and you know the vocabulary to ask AI for - or you have the meta-prompting skills to get AI to reflect on what skills and background it needs - you can bring any idea to life within what AI is capable of producing.

Here’s the thing though. A lot of people use generative AI for mundane tasks: writing blog posts, drafting emails, that kind of thing. There’s nothing wrong with that - they’re practical and useful. But they don’t illustrate the art of the possible.

Nobody is wowed by another blog post.

Those are practical, tactical, useful things, but they’re not sexy. So what does wow people?

Making a highly styled infographic or interactive that uses your brand guidelines. Building a browser-based video game - I did that once for an architecture industry talk, taking a super boring RFP response and turning it into a little browser-based video game using the Phaser library. Something fun. Something light. Something that opens people’s minds to “I didn’t know I could make that.” Anytime you make a video game, people are wowed by it. That’s a pretty straightforward reaction.

Another example: writing an academic paper. I wanted to validate whether LinkedIn was biased in how it reports feed results. Instead of gathering anecdotal evidence or setting up badly controlled tests, I gathered all the technical papers and engineering blogs from LinkedIn’s engineering team, replicated their system as closely as possible, ran tests with actual platform data, and ended up with a statistically valid, rigorous paper published on Zenodo with a DOI number. People didn’t think “I could have generative AI help me build a full-fledged research paper.” But you can. That’s good quality research that frankly had not existed until that point.

And here’s what really drives home the Create category: when people see solutions that they didn’t know could be solved. Problem solving and creating things to solve problems is sort of AI’s magical power. The wow comes from watching an idea become real in minutes instead of months.

Part 2: Refine - Extending and Modernizing What Exists

Refine is about taking any existing thing and extending it. Improving it. Bringing it up to modern spec. I use Claude Code heavily for this.

Here’s a concrete example. There’s this piece of software called iMouseTrick - a high-speed clicker I use in video games. It was made in 2010. When I upgraded my Mac recently to Tahoe 26.2, it finally stopped working. Sixteen years is a good run. So I sat down with Claude Code and said, “Here’s what the software did” - took a screenshot of the interface - “here’s its purpose, let’s build this in Swift,” which is the modern language for macOS.

In 45 minutes, I had not just replaced that software but extended it, refined it.

I did a deep research project to gather best practices for Swift applications. I made it compliant with modern standards. I added new features: jitter in the timing between clicks so it wasn’t exactly the same interval, the ability to save preferences so you don’t have to redial settings every time - whatever the last settings were that you used, it remembered them. A click counter that showed how many clicks it had made. Little things, but all things that extended the application beyond what it was.

Another example: Google released a tool to show how query fan-out works, built in both React and Python with a front end and back end. I thought that was unnecessarily complex, so I had AI move it all into Gradio. But I didn’t stop there - I wanted it to replicate what AI Overviews and AI mode does, and what deep research actually does, as closely as possible. I gathered all the relevant papers, patents, and public statements from Google, figured out what was reusable and what needed extending, and looked at what else was available in the various APIs that we could use to make the tool closer to reality.

It took many iterations. But eventually I created not just a replication of the query fan-out procedure, but an auditor tool that shows what’s happening at every stage so marketers can understand how Google’s system makes decisions. Because it uses Google models under the hood, based on whatever Google has said they’re using behind the scenes, it’s as close to reality as we can get.

As far as I can tell, it’s the best GEO tool for understanding what an LLM-powered search is looking for. Every other tool on the market tries to go for fast, easy answers, and I think that’s stupid because language models are not fast, easy systems - they’re thinking systems, reasoning systems.

One more: there’s software called Opus Clips that’s pretty good, but I wanted it to do things my way. I don’t like its summarization, I don’t like the way it decides things. An open source project called Superclips was trying to reverse engineer it, but it didn’t behave how I wanted. So I took that MIT-licensed project (I have the right to do this, I’m not violating anyone’s intellectual property) and extended it more - added my logo, changed fonts, improved the detection system for relevance, swapped in a different transcription model. A lot of keeping the idea but changing out the guts.

That’s Refine. You’re not starting from nothing - you’re starting from something and making it significantly better, making it yours.

Part 3: Arrange - Bringing Order to Chaos

Arrange is about taking lots of stuff, lots of data, and turning it into something useful. Bringing order to chaos. This is where agentic AI becomes critically important.

Last week’s newsletter was about using AI to review your strategy - taking your ideal customer profile, turning it into an agent, and having it review your marketing plan. Creating agents for your CFO, chief revenue officer, and CEO, having them argue about your strategic plan autonomously until they reach consensus on what you should be doing to achieve business goals. All of that is bringing order. Taking the chaos of data and strategy and making it actionable.

Here’s the reality. We’re swimming in data, and yet we don’t do anything with it. Or worse, we create a strategic plan and then it just sits there - we run out of time, we don’t make the effort. How do we use AI agents to pick up that slack?

Real example: I wrote an academic paper that I want to submit to different conferences. I don’t want to fill out the same form over and over. That’s stupid. That’s an automation task. With a Playwright MCP server that allows AI to use a browser intelligently, I can have Claude Code apply to each conference, ask me questions when it encounters fields it doesn’t have answers for, gather up the information, and then submit everywhere I want to apply.

Another example: there are great conferences that should hire me as a speaker. What if I just had Claude Code with multiple agents go out and apply to those things using Playwright and browser automation? Instead of me copying and pasting, which is pointless, the tools can do it.

When I’m preparing for workshops, I give a set of Claude Code agents my general use case categories and the industry and maybe some sample companies, and it creates synthetic data for the workshop. This used to take me hours and hours to synthesize manually, and now Claude Code just goes off and does it. The tools handle the tedious work so I can focus on what matters.

This is where agentic AI becomes critically important. We think about Create and Refine as producing deliverables that you then have to go do something with. With Arrange, we’re taking things that are done and saying “go do something else with them.” A tool with browser use capabilities can check your email, fill out calendar appointments, handle all that trivial stuff.

Even small models like Qwen3 VL - because it’s a vision language model, if you put it inside a browser use harness, even though it’s small and fast and not terribly accurate, if you’re giving it the data and just saying “follow these instructions and fill out this thing,” you’ve got an agent.

We define an agent as something that does the work for you. A real estate agent buys or sells the house for you - there’s very little you need to do. You don’t need to go solicit buyers or sellers. A travel agent handles bookings and itinerary, sets up reservations, does all the stuff. That’s what we mean by agent. A lot of what people call “agents” in AI right now are really just workflows at best.

The wow factor in Arrange? Watching the tools do the thing. You can see: “This just did the thing. I did not have to do this.”

Part 4: Flip - Transforming Across Domains

Flip is about transformation. Taking content and converting it into something completely different, often across domains. Turning one thing into a completely different thing.

What about an old photo? Let me colorize it in Gemini’s Nano Banana Pro, then bring it to life in Veo 3.1 and animate it. I did this with a photo of my mother when she was 21 years old in a store in New York, taking that photo and just bringing it to life. For an event in Edmonton, I took a 1912 photo of one of the first Calgary Stampede parades, colorized it, and brought it to life. Now people can watch that procession move down the street, see the event unfold.

That’s the kind of thing where people say, “I did not know it could do that. That is incredible.”

Notebook LM can create infographics, video overviews, and podcasts from your documents. You can make your own conference - I’ve taken academic papers from conferences like NeurIPS or ICLR and turned them into a course for myself. “Here’s what I want to learn based on these papers, what are the top 25 new prompt engineering techniques I should know?” People look at that and go, “Oh, that’s how you get smarter very quickly.” You have the tools summarize, extract, and then rewrite - going back to those seven use cases - into whatever format works best for your brain.

That’s another wow outcome: taking free knowledge, stuff that people give away for free, and transforming it into something that fits the way your brain works.

Anytime you turn one thing into a completely different thing, that’s Flip. Turn a boring RFP into a rendering. Turn that rendering into a YouTube video with a drone aerial flyby shot. Turn mission statements into country songs in minutes - I’ll take somebody’s mission statement or their RFP and turn it into a country song live, and that always goes over well. All of these transformations bring ideas to life in unexpected ways.

The impressive moments in Flip come from seeing that scale of transformation. “Here’s something that used to take six months; now it takes six minutes.” Anything that involves scale is a massive moment. The first time we used the PowerPoint skill inside Claude and it made a slide deck on the fly, Katie was wowed by that - the fact that it could even do that now, whereas previously it didn’t have the ability. It opens people’s minds to what’s possible.

Part 5: Tackle - Autonomous Problem Solving

Tackle is about taking on tasks and completing them autonomously. Solving real problems.

One of the biggest issues with generative AI is that people don’t see how it solves the problems they actually deal with. They see the technology, the buzzwords, the techno-babble. They don’t see: “I have this problem. This tool can build me the solution.” Building apps, building stuff that has functionality and solves a real problem - that’s what creates the wow.

Sometimes the problem’s pretty simple: “I have a CRM, I spent too much money on it, I want one that costs less.” I can solve that. AI is really good at copycatting - you give it screenshots or a spec, and it solves it. Someone will look at that and go, “Holy crap, you just made a whole CRM in a day and a half.”

I did that recently with my blog. There were five or six WordPress plugins I was paying for, and I thought, “This is stupid. Why am I spending money on these things?” So I had AI write a combo plugin that uses fewer resources, has less bloat, and gets the job done. That saves me money, and people go, “I didn’t know it could do that.” Yeah, it can write software for pretty much anything.

Where it gets really interesting is novel problems. The famous example: Spotify’s CEO Daniel Ek got medical imaging done and received a USB stick with his data on it that he couldn’t read. So he had Claude Code build a reader. Instead of paying thousands of dollars to some medical software company, the software replicated the intended functionality to solve that particular problem very, very easily. Any problem that’s novel, as long as you have the vocabulary to explain it to AI, can be solved this way. You give it the general principles, and AI can do the research, pull together the pieces, and build the solution.

Extraction wows people too. Turn on screen recording on your phone, scroll through a social media app, and have Gemini transcribe everything it sees. “I didn’t know you could get data out of that system.” Getting data out of systems that do not want you to have the data - if you can see it, you can grab it.

Very popular with sales and marketing folks: lay out a bunch of business cards on the floor, take one picture, and have image recognition transcribe them all. You used to need special software or spend hours scanning cards one by one. Now you take one picture and do 25, 30, 50 cards at a time with high accuracy.

Real-time translation is another Tackle example. I built a real-time captions bot using a machine translation model - now I’d use Translate Gemma for real-time transcription in other languages with high accuracy. Just being able to show AI building that locally is so cool.

Here’s another one that created an amazing moment: Katie and I were organizing survey data, and we did sentiment analysis with Gemini inside of a spreadsheet. The impressive part wasn’t just the analysis itself - it was how accessible it was. Previously, sentiment analysis required a lot of effort, specialized tools, expertise. Now you can do it right in your spreadsheet. The tools have democratized capabilities that used to require significant investment.

And here’s something from strategy and consulting: you used to pay a consulting firm like McKinsey hundreds of thousands of dollars for strategic analysis. Now, with those same resources and those same processes and those same concepts, you can do the same thing for yourself for pennies. That’s a massive revelation for people.

Part 6: Wrapping Up - The 3C Skills and the Artisan’s Edge

CRAFT - Create, Refine, Arrange, Flip, and Tackle - gives us a framework for thinking about AI outcomes, not just AI processes. It’s about demonstrating the art of the possible, the things that make people say “wow.” It helps us understand the different kinds of outcomes as a magic show of what’s possible.

But here’s what matters most: these capabilities can also cause concern, because people realize the technology accelerates so fast that what they thought was a safe domain is no longer safe. It’s something that can be consumed. The impressive moments are double-edged - they inspire, but they also make people realize how quickly things are changing.

One of the things I say in my lectures often: generative AI provides a baseline level of skills. It’s not master level - though it’s getting there. You need the vocabulary and the ideas, the creative thinking, the critical thinking, the contextual thinking to bring things to life.

If you have those 3C skills - creative, critical, and contextual thinking - you know when to call bullshit on a machine. You have better ideas, more ideas than anyone else. You know where all the data lives that you need to bring something to life. That’s when you can create those really incredible experiences.

Creative thinking means you have the ideas in the first place - you can envision what’s possible. Critical thinking means you can evaluate what the machine produces and know when it’s wrong, when to push back, when to iterate. Contextual thinking means you know where all the data lives, what’s relevant, what the real-world constraints are. Without those three skills, you’re just pushing buttons and hoping for the best.

When I was rebuilding a client application, the first draft took six months. The second draft took six hours. We had so much more knowledge, better vocabulary, and better thinking about the processes. That’s the artisan’s edge - not just using the tools, but knowing how to use them with skill, judgment, and vision.

One more thing I want to leave you with: I want to do more of this in workshops this year - create those impressive moments for people to try themselves. Pre-baked data and setups so they don’t have to do the boring part, but they can give it a shot and go, “Okay, that’s pretty cool that I was able to do that thing.” Because the real transformation happens when you stop watching someone else do it and start doing it yourself.

The art of the possible isn’t about the AI. It’s not about who you are, it’s what you do with it that defines you.

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