You Have to Try it To Need It
On our resistance to trying new tools
The below audio is an AI-generated podcast about this article:
I’m always a little surprised to find resistance in myself to trying new tools.
It’s an easier pattern to notice now that things are moving so quickly. New tools present themselves so often, that it’s impossible not to dismiss them frequently.
For instance, when a new AI code editor named Cursor came out last year, I saw many people hyping it. I ignored it for a good while, but then I tried it.
Was I immediately blown away? Not entirely. But I was impressed, and as I learned the features and thought about how to use them to work more quickly, I realized how powerful it was.
Now it’s the only coding tool I use.
It happened again recently. I’d been seeing chatter about a new tool from Google called NotebookLM, where you can upload multiple PDFs and generate an AI podcast about them.
The podcast is stunningly realistic. You can hear the AI podcast voices breathing, and they make a bunch of little quips and meaningful interjections.
I thought it was cool – but I don’t listen to podcasts, so I didn’t use it.
Until my manager sent me six long research papers to read on AI in finance. I figured this would be the perfect thing to turn into an AI-summarized podcast to listen to while I hit the gym.
But the mere act of using this tool gave me another idea: What if I used this to generate a podcast about my employer?
So I gathered up every case study from the company, and within 10 minutes had an incredible podcast of 2 people gushing over how great the company and its product was, while succinctly conveying all of its benefits and value propositions in a highly-digestible manner.
The CEO called me late that night after listening to it, completely astonished. He informed me that getting a podcast produced like this would involve hiring a media production company for $60,000 - $70,000, with at least 3-4 months of back and forth preparation and edits with feedback.
And just like that, an immense amount of marketing value was created.
But what I’m really trying to get at here is that discovery is a creative process. And when new tools come along, we actually have to use them to know what they are.
But not just that. While using these tools for the first time, we must actively be thinking of new things we can do with them. I’d never thought to produce a podcast about finance before, and I’d never looked at the company’s case studies before, but I knew the studies were available, and knew I could suddenly do something with them.
With so many new tools coming along, the best way to utilize them is to ask ourselves “What do we have available that can make this tool do something interesting? Something unique? Something valuable we could never typically afford or produce ourselves?”
And in this I emphasize that we should not just only use tools that are handed to us, and use them only as we are told.
As an example, an engineer recently told me they only use ChatGPT to code. They never thought to use it for anything else.
This was astounding to me. ChatGPT is trained on almost all the knowledge of the internet. It’s incredibly useful. I rarely ever even need to Google things anymore, because it often gives me an immediate and direct response tailored exactly to my situation.
What’s going on here is that often with new tools, people tend to silo them in the context they first use them. If someone learns to use a tool for their job, they’re liable to end up thinking that tool is only useful in the context of that job.
The way to get around this is ask oneself “How can I use this tool in other ways? Can it be used in other ways?” Sometimes the answer is no, but to discover the truth, one must experiment. And part of this experimentation is actually pushing tools to their limits.
It’s important to do this, because then you can find out what the tool can really do. Maybe it can’t quite do a treacherous task today, but if it almost can do it, it probably will be able to actually do it in a couple of months. It’s personally useful for anyone to stay on top of those changes when they occur.
It can be hard to do this, because it isn’t intuitive to us. A hammer is a hammer, a drill is a drill. An AI that can think, understand, and do anything is not something we are used to.
This is why we must model our tools accurately. And the best model for AI is not just “tool”, but “graduate-student-level across all domains personal human virtual assistant”. You ask for what you need, you get very specific, and you often can get a very informative and useful response back, across almost any domain.
For instance, once upon a time I had never barbecued something before. I simply uploaded a picture of my new grill to ChatGPT and it walked me through it and gave me recipes.
But the other day I also built an AI browser extension that analyzes open tabs and groups relevant ones together, adding a summary title. Had I ever vectorized text and clustered it together like a machine learning engineer before? No. But openAI’s o1 and 4o models could walk me through how to do it.
And another time ChatGPT walked me through fixing a broken valve on my toilet.
We’re really just not used to it, but we’ll have to get used to this idea of the versatile assistant able to help us across any domain, and we’ll need to start thinking with an AI-first mindset.
This means, with any task we have, we ask ourselves first: can AI do this? And it involves making an earnest effort to get AI to do it, and even chain several tools together to make it happen.
For instance, someone once asked me to make a video of someone speaking and flying up into the sky. I cloned their likeness using PhotoAI. I cloned their voice from some video clips using ElevenLabs. I turned generated photos of them into video using KlingAI, and then used their dubbing tool to lipsync the generated person in the video to say something generated from the ElevenLabs cloned voice audio. Then I used an AI video upscaler to enhance the final quality of the video.
The point being, when we reach for AI first for all aspects of what we want to achieve, we can go much further, much faster, in areas totally unfamiliar to our own backgrounds.
Btw, I’ve embedded a generated podcast about this very article right at the top. You can see what I mean.