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,
“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 instant call notes
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.
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
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.