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If you're not excited about ChatGPT, then you're not being creative

·9 mins

The hype around AI in general and ChatGPT, in particular, is so intense that it’s very understandable to assume the hype train is driving straight toward the trough of disillusionment.

But I want to challenge your instinct toward skepticism: If you’re not excited about ChatGPT, you’re probably not being creative enough. And if you are excited about ChatGPT, you’re probably still not excited enough.

In this article, I’m going to make the case for that excitement. My argument is this: ChatGPT, as it stands, is cool but the potential is most visible when you identify its two primary blockers: Your creativity and the ecosystem’s integrations.

ChatGPT is a window #

The simplicity of the ChatGPT interface is welcoming but its simplicity makes the whole project easy to underestimate.

Take AI-assisted writing, for example. Many of the first headlines when ChatGPT debuted were about how we’d either be automating all writing or about how bad ChatGPT is at writing and how there’s no way we could automate writers. Both angles miss the point.

The problem is that prompts are hard – both because it takes some practice to learn how to get ChatGPT to create what you want and because the most exciting prompts requires creativity.

Nat Eliason provides a good example.

In an article on using ChatGPT as a writing coach, he demonstrates the process of using ChatGPT to help him write a section of his novel.

The first prompt – “write a vivid description of someone getting out of bed” – falls flat, but it includes a couple of details he can use in another draft. Next, he asks ChatGPT to rewrite his attempt at writing the section.

This isn’t impressive either. What Nat does next though is incredible. He asks ChatGPT to write the section from scratch like the famous novelist David Foster Wallace.

Next, he asks ChaptGPT to critique his original draft as if it were David Foster Wallace:

Nat does a few other cool things, such as asking ChatGPT to increase the suspense and add more detail, but the point of these examples is to demonstrate how high the ceiling is if you’re willing to be creative.

You don’t need to be a writer to be excited. The excitement comes from looking at the early use cases and imagining parallel creativity. For every use case that emerges, there will likely be a similar David Foster Wallace technique you can deploy.

I know it’s cliche, but it’s a cliche because it’s true and I’m going to say it anyway: This is only the beginning.

Patrick McKenzie, better know as Patio11, writes that “most people who see ChatGPT and think ‘Huh, neat, but not really more than a toy’ aren't playing forward the 6-18 months it takes scaled product teams to start integrating LLMs into a few pilot projects and then 2~4 product cycles, after which it will be in *so much.*”

The David Foster Wallace technique is cool but it’s the result of one person’s brainstorming. Patio11 goes on to say that “there is an adoption cycle among developers / product people / companies just like there is an adoption cycle among consumers.” As builders adopt AI, implement AI, iterate on the AI products they build, and develop and share best practices, the possibilities will stretch beyond what we can realistically imagine.

Integrations will be an exponential multiplier #

Andrew Ng, computer scientist and co-founder of Coursera, compares AI to electricity: “Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.” This is another comparison that builds hype and invites skepticism but it's valid not because it’s literally true but because it’s directionally true.

If you focus only on what you can do within the ChatGPT chat interface, you're limiting your imagination. Like with electricity, your predictions would be entirely inaccurate if you focused only on powering lightbulbs.

As Patio11 said above, there’s an adoption cycle amongst developers and companies just as there is amongst consumers. But the result of that work won’t just be pure AI apps – there will be a multitude of integrations small, large, and transformative.

At present, Notion has implemented an AI writing tool, Quizlet has created an AI tutor, Shopify has made an AI shopping assistant, and if you’re hooked into the AI community on Twitter, you’ll see amazing stuff every day, such as people using AI to make instant call notes and to use documentation to automatically answer customer support questions

These early integrations are exciting, but what’s even more exciting is the seeds of exponential progress being sewn – progress that will come from the nature of AI development and the results of collective creativity.

As Evan Armstrong writes, AI product development doesn’t play by the same rules as traditional product development. Traditionally, product development involves a steady and mostly linear refinement of materials and features. With AI development, in Evan’s colorful worlds, “AI is a snake eating its own tail because the various components feed off of each other.” As companies build integrations and data stores, progress can take unpredictable leaps forward if, for example, improving training data also improves access points.

Evan also points out that the AI industry is “rooted in academia,” which means that “whenever a company launches a new product, they typically publish an accompanying article in a major scientific journal outlining the math, data set, and process they used to create the model.” Advancements in AI have been and will likely remain extraordinarily accessible to companies and lone hackers alike.

That kind of collective creativity can produce incredible results. At a large scale, we’ll have companies building AI products that can integrate with and feed into other AI products; at a smaller scale, we’ll have examples like the one I cited in the previous section – using novel techniques, such as calling upon AI David Foster Wallace, to use existing tools better.

At first glance, this is the kind of creativity that already feels familiar. In the tech industry, especially, we’ve long shared new tools, tips, and tricks with each other. But that first glance is missing the new player: AI. Already, ChatGPT produces novel, unpredictable results. At scale, across a multitude of integrations, and after different kinds of training and fine-tuning, no single person will be able to keep up with all.

As I was writing this article, OpenAI introduced the ability for developers to integrate ChatGPT and Whisper models via API. Maybe even more significant is that OpenAI slashed prices with ChatGPT now costing $.002/1k tokens (ten times cheaper than GPT-3.5) and Whisper costing $0.006 / minute.

As costs drop, momentum builds: More people can try and more companies can integrate and as everyone experiments, more people can see the potential and buy in.

Examples are everything #

The biggest temper on AI hype is the sheer overload of it all. It’s already effectively impossible to keep up with all the progress being made, especially given the fact that every new product and integration will have undiscovered, creative techniques beyond the use cases even the creators might have imagined.

My recommendation is to pay attention to the builders and not the growing cottage industry of hype. You’ll inevitably hear endless stories about how AI will be our salvation or our doom and about products that promise more than they deliver.

Watching the builders is more exciting because the practical realities of building actual products will lead to more innovation than people prognosticating from the sidelines.

I humbly submit our work here at MermaidChart as an illustrative example.

At Mermaid Chart, we've made it so you can use OpenAI, at the click of a button, to either generate a diagram from text or generate a text that summarizes a Mermaid Chart diagram.

In the below image, our Projects view, you can press the highlighted button to open a dialog where you can enter text that the AI can use to generate a diagram.

And in the next image, you can see the dialog for generating the diagram.

In the next image, you can see the generated diagram.

You can then click the highlighted button in the below image to generate a summary.

If you were only looking at the flashy images produced by models like Stable Diffusion, you might reasonably assume AI can’t produce a precise diagram. But because Mermaid Chart offers a way to transform text into diagrams, an integration has made AI-generated diagrams possible.

With new integrations emerging every day, from new companies and established companies, the possibilities are both endless and unpredictable. Excitement is warranted, but you’ll want to ground it in watching the builders, not the tweeters.

Winter isn’t coming #

There’s precedent for the collapse of AI hype – there’s even a name for it: AI winter

The first AI winter occurred between 1974 and 1980. The second AI winter occurred between 1987 and 1993.But just as important is the AI spring that happened after each winter.

From 1980 to 1987, after the first winter, expert systems emerged while Deep Thought and Deep Blue took the chess-playing stage. From 1993 to 2011, after the second winter, intelligent agents emerged while a Stanford robot drove autonomously for 131 miles and Watson defeated two Jeopardy! champions.

From 2011 on, we saw the rise of deep learning and big data. And now, OpenAI, ChatGPT, and the veritable Cambrian explosion of ChatGPT integrations.

Even now, skepticism still isn’t entirely unfounded. You could point to salient counterexamples, such as the continually hyped and punted era of autonomous cars, or to the stat that 85% of analytics, AI, and big data projects fail. But you could also point to companies like UiPath, which has made billions producing robotic process automation software informed by ML and AI, and scientific advancements like Alphafold, which can predict a protein’s shape better than biologists can.

The failure of AI is possible, but so is its success. And that’s ultimately what excites me the most. It’s not that there aren’t problems with AI. It’s that even the problems are exciting. When I saw the screenshots of the Bing AI chatbot going around – you know, like the one where the bot said it wanted to be alive – I felt a mixture of things.

A little Eliezer Yudkowsky-inspired concern is probably warranted, but so is a little laughter and amusement. What I come back to though is the fact that AI did something we couldn’t predict. Given the creativity of human inputs and the extensibility of product integrations, the only thing we can reasonably predict is that we’ll be surprised.