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This is Reinamoto's podcast, The Creative Mindset.
Welcome to The Intersection, a new segment and audio version of my essays exploring what the
future holds at the intersection of creativity and technology. I am Reinamoto, the founding
partner of I&CO, a global innovation firm based in New York and Tokyo. Based on the conversations
that I have with the top creative practitioners from various industries, I write a weekly essay
to dig deeper and analyze where we may be headed as creative and business professionals.
Although we talk a lot about AI and technology, we'll be asking a human voice actor to record
these episodes. We'll be bringing this segment as a bonus episode to you. So let's get started.
How to AI. 11 out of 12 faces are generated by AI. Which one is the real person? I showed an
image of 12 faces and posed this quiz as an icebreaker for an executive meeting at a major
international brand. The CEO of this company had asked me to give a talk to their 20 plus
top executives about where AI is today and where it could go. No one got it right.
After the talk at this Fortune 500 company, one executive commented,
showing skepticism toward what AI could do for the company, its employees, and its business.
Are we so busy that we need to rely on AI? He said. Oh boy, my icebreaker didn't land, did it?
I was startled by this comment a little. That quickly subsided. I've seen this script.
History doesn't necessarily repeat itself, but it rhymes. 20 odd years ago, when I started my career
in digital design, corporate executives, particularly in marketing, had similar skeptical
stances towards digital. Even in the mid to late 2000s, when there was a growing and healthy size
audience online, some marketers were still dismissive. I was a digital creative director
back then at a creative agency. Anyone above 40 in the industry would remember that our
digital stuff used to be given a graveyard shift in a presentation deck. In one client meeting with
a CMO, after the traditional work was presented over 45 minutes, it was the turn of the digital
team. Okay, digital, cheap and quick stuff, said the CMO, chuckling. Among the non-technology brands
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that I work with, and what I hear from others, it's a mix of elated embrace of and uneven
interpretation of AI. Many of the brands that have embraced AI, particularly generative AI,
have done so more for the sake of PR. To be clear and fair, this is not my criticism or cynicism.
At least they are experimenting, and experimentation can lead to unexpectedly good outcomes.
The others that haven't are cautious because of data security, plagiarism, fake work,
and unpredictable hallucinations. This is also understandable, as these issues can have serious
liability consequences. There is a general confusion around how to start and where to deploy
AI, even though they know they should. Embracing new technologies early has its upside,
as we've seen in the case of NFTs and the metaverse in the recent few years.
At the same time, their success can descend as quickly as they ascend. In 1955, then 29-year-old
engineer John McCarthy used the term Artificial Intelligence in his writing.
AI has been around the block for quite a while in various forms. Up until several years ago,
there was a varying degree of predictions around Artificial General Intelligence, or AGI,
AI that is general enough that perform a wide range of tasks like a human,
instead of a specific one, and the possible timing of it becoming a reality.
Some AI experts predicted it would be around 2030, others said 2050, and few predicted never.
ChatGPT, other LLM systems, and other generative AI tools may change that timeline more drastically
than the experts thought. Here's a quick look at the several waves of AI evolution.
1. Artificial Intelligence, the first boom in the 1950s to 1980s.
2. Machine Learning, the second boom in the 1980s to the 2010s.
3. Deep Learning, the third boom in the 2010s to the 2020s.
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4. Generative AI, the fourth boom in the 2020s and beyond.
With Artificial Intelligence, systems became capable of performing sophisticated tasks
that would require human intelligence.
With Machine Learning, systems could learn and adapt while analyzing patterns in data.
With Deep Learning, systems evolved to use multi-layered neural networks for learning
and adapting from data. And finally, now with Generative AI,
systems can now learn with large amounts of data and generate creative outputs.
The current AI situation is chaotic. However, as was the case with personal computers,
the internet, and smartphones, it is gradually converging. It is an exciting time. There are
numerous tutorials, classes, and self-proclaimed experts and gurus, as well as optimists,
pessimists, and skeptics. At an individual level, it is useful to start playing with the tools to
see where it leads us. However, at a corporate level, it is necessary to frame the challenge we
face in a strategic way to make some sense of the way forward. I, with the help of my team,
organize the current situation in the following framework. Operation AI, Creation AI,
and Transformation AI, each of which will afford a benefit.
First, Operation AI. When talking about AI today, the most obvious and immediate area of
deployment for corporations is operational. It gives us ways to gain efficiency in our daily
tasks. Such tasks include transcription, interpretation and translation, preparation
of meeting minutes, text summarization, creation of slides, materials, manuals, etc., sending emails,
and data analysis. GANMA, AI for Presenting Ideas Beautifully, is one example of an operational use
of generative AI to supposedly improve productivity. One of the Achilles' heel challenges
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of generative AI is that it requires a massive amount of data. Big tech companies like Google
and Microsoft are at a major advantage as they have access to data and millions, if not billions,
of people already using their productivity tools, such as Word or Docs, PowerPoint or Slide,
and Excel or Sheet. Generative AI can be incorporated into this seamlessly.
If there are tasks that we are repeating, they probably can be replaced by AI.
Speaking of repeating tasks, image generation for e-commerce is one such area. Retail companies
outsource that work to other more cost-effective vendors, but that will be changing, if not already.
Try-On Diffusion, an initiative from the University of Washington and Google research team,
shows great and convincing promise for the very near future of an aspect of shopping.
What this tool does is to take two images, one a person and the other a garment worn by another
a garment worn by another person, and diffuse those two to, quote,
generate a visualization of how the garment might look on the input person, end quote.
What this would allow retail brands to do is not just generate images of the products on the fly
on various models, but also what the product might look like on the customer.
Operation AI brings new speed, the kind of efficiency and productivity to us.
The strength of AI is not that it gives correct answers, but that it gives us speed. Everything
can be executed at high speed. However, that does not mean that it's correct and humans do need to
intervene to confirm the output. Second, creation AI. Writing and visualizing used to be a skill
reserved for humans. Not only did that change drastically with generative AI, but it's also
improving exponentially, as we've all seen visual comparisons across the past few months.
In addition, in understanding the current state of generative AI as it relates to creating various
forms of content, Sequoia Capital's table on the progress of generative AI is useful in quickly
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getting a sense of where we are and where we are headed. This table is, however, from September
of 2022, and given the speed at which AI is progressing, some of these predictions may be
outdated. Some predict that the majority of content online will be synthetic shortly.
That might also be true in the physical world sooner rather than later.
The Airgon line of sneakers is an experiment by independent designers and artists for now using
generative AI to create endless options. We can easily imagine that there is already a new crop
of brands we haven't heard of that leverage AI to generate numerous designs based on what's
trending online. If quantity and variety are the selling point, like in fast fashion,
creation AI could prove helpful. The next sheen is probably on its way.
The majority of generative AI tools tend to skew towards operation AI rather than creation AI.
That is, they promise to increase efficiency rather than effectiveness. Taplio, a social media
content creation and planning tool focused on LinkedIn, is one such case I've seen. Its authoring
tool, based on your original writing, suggests how to rewrite for better hooks and better
engagements, and gives specific reasons why the rewrites it gives would be more effective,
allowing the author to improve as they use the tool.
A recent collaboration between Claire Silver, an AI collaborative artist, and another fellow
digital artist, Emi Cassano, is an attempt at imagining the new possibilities. One no longer
needs to have design skills to be a designer. Silver says, the barrier of skill is swept away.
In this evolving era, taste is the new skill. Silver and Cassano on their own may not have
been able to create these images. That barrier is now indeed gone.
Jonas Pedersen is another photographer who turned to AI recently for his work.
Pedersen's strength as an image maker is the narrative he creates behind his work.
His previous line of work was wedding photography, but if you look at his earlier wedding photography
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work, it has a distinct feel that sets it apart from other run-of-the-mill photography out there.
His photographs look like scenes from movies, which tools like Midjourney are decently skilled
at producing based on prompts. He's now using his taste to apply to AI art.
Creation AI raises the bar, creating a new standard. Creation AI's strength is not in
generating ideas, but in raising the average that humans can create. It democratizes and
deindustrializes creativity.
3. Transformation AI
The opportunity that generative AI affords us is to rethink how we can operate our businesses
and transform them. Throughout the centuries,
we have seen transformations in media and how we relate to the world around us.
What this evolution brings to brands and corporations is that, in addition to the new
speed and the new standard, it also allows us to reach the kind of reach and scale never possible
before. In the age of the internet and mobile, technology allowed not only corporations to
connect with individuals, we were able to connect with brands on our terms. What each of us saw from
a brand, however, was mostly the same. That's because the communication was either mass-produced,
templated, or pattern-matched. This can change radically in the age of AI.
4. The representation of a brand no longer needs to be singular. A brand can and should have one
consistent voice, but with it, it can also cater to the individuals separately and specifically.
Take HYBE, the Korean entertainment juggernaut behind enormously successful BTS and other K-pop
groups, as an example. They announce the first single,
Masquerade, from Midnat, a new artist that the label is backing. What was new and unique about
this debut song is that it was produced in six different languages, Korean, English, Japanese,
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Chinese, Vietnamese, and Spanish. It's a good example of a partnership between humans and
machines to achieve reach. By releasing the song in multiple languages, HYBE is enabling the kind
of scale for its artists that wasn't possible a few years ago. This isn't specific to the individuals,
and it probably doesn't and shouldn't be. A song from an artist is something we share
and we aren't looking for some hyper-personalization.
Let's take this another notch by the thousands.
During the UEFA Champions League Final, Adidas created a fan experience by transforming football
legend Alessandro Del Piero into their personal newscaster and commentator.
Using WhatsApp AI, fans could chat directly with Del Piero, and they would receive live updates and
replies instantly, from the star himself or the AI version of Del Piero. It was at a scale that
It was at a scale that was never possible before, over 44,000 messages in 10 seconds.
The idea of personalization has been around for years now. However, personalization executions
have mostly been about serving pre-generated content to the individual user based on the
tracking of their previous patterns. Similarly, Chris Do, a design entrepreneur with more than a
million followers on social media who helps designers become better business people,
is creating an AI version of himself to allow him and his team to serve a much bigger audience
with a credible replica of Chris himself. This type of deployment of generative AI
makes true personalization possible, in that it's not based on the user's prior behavior
or engagements. In addition, AI makes it possible to tailor that unique interaction
to mass and scale instantly like never before. Transformation AI can give us the kind of reach,
new scale, by allowing us to do more with less. Transformation AI's potential is to help us
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grow our business without growing our organization significantly. It scales our reach way beyond our
individual as well as organizational capability. It could transform the way we manage our business.
Back to the icebreaker quiz. Most people think number 7 from the image in my newsletter
is the real person, followed by number 4. The answer is number 5. The tell is her ears and
earrings. For generative AI, ears are, similarly to the fingers, apparently difficult to render.
If you look at number 5, her ears and earrings are accurately and realistically shown.
Well, it's a real photograph. The image is courtesy of Imaginavi Inai Model, a Japanese
imaging company that started offering a virtual modeling service using AI to replace human models
with generated models. The real photo is by Jimmy Furman. I also asked, at the aforementioned meeting,
other than ChatGPT, if anyone had tried using generative AI tools such as MidJourney,
Stable Diffusion, or others. Most hadn't, and I don't blame them. The interfaces of some of
these tools are still pretty atrocious. One of the issues of newer technologies like
VR and the metaverse that's stopping mass adoption, I believe, is the interface.
For them to become widely adopted, the interface needs to be so simple, like ChatGPT or the iPhone,
that anyone can use it without manuals or tutorials. Unlike the metaverse or NFTs,
generative AI is here to stay. Enough applications of generative AI are becoming available literally
on a daily basis. In sum, the three approaches to deploying generative AI for brands and
corporations are Operation AI, a tool for improving efficiency to achieve new speed,
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Creation AI, a tool that raises the average and elevates us to a new standard,
and Transformation AI, a tool that transforms the status quo and allows us to reach new scale.
Having said all of this, I do sympathize with the executives' skepticism, at least partially,
as much as I do rely on ChatGPT, Claude.ai, MidJourney, and other AI tools for various tasks
throughout the day, I, and not AI, am still writing this newsletter entirely by myself,
occasionally with some help from other humans at my company. If you knew it was being written
by ChatGPT, would you still read it? I started writing this newsletter last summer in 2023
as a way to organize my thoughts that come from various conversations with many creative
practitioners. Over the course of several months, I see and notice new patterns and new insights
gained from multiple people, and I take the time to organize and write them down so that they can
be useful and hopefully helpful to you as listeners and readers. If you're listening
to this on Spotify, there's a Q&A field, so please do send us your questions and comments.
And if you like our podcast, please leave us a 5-star rating. We'll be so grateful.
I'm Reina Moto, and this is The Creative Mindset. See you next time.
