Welcome to another edition of the Play Bigger Newsletter.
Today we’re talking category design, AI, and troughs of disillusionment - read on as we:
Ask ChatGPT about its plans for the future …
Look at two AI startups nailing the development phase of their categories...
Hear the SEC’s take on AI’s fake-it-til-you-make-it set (TL/DR: they’re not fans)…
Let's get into it...
1. Why AI Hype is Falling Faster Than the Downleg of Kingda Ka
Remember when Gen AI was blowing the doors off earnings reports and single-handedly propping up public and private markets wayyyyy back in three months ago? Turns out those heady days are over and it’s time to be disillusioned by the crumbling of your inflated expectations. Or so says Gartner, who last week shared that, yes, we are indeed in the AI Hype Cycle’s Trough of Disillusionment.
That trough, as dark and scary as it may seem, also marks the beginning of a new stage of the category lifecycle (take a look at the diagram), or dig in deeper here. Say hello to the Gen AI ‘DEVELOP’ phase and goodbye to a whole bunch of companies swirling around it, as HYPE gives way to SUBSTANCE.
This is the moment when “sexy new shiny tech thing I can tell the BOD we’re capitalizing on like the champs we are!,” moves to “what problem does this thing actually solve and why are you paying so much for it?!”
Great point, board.
It’s why at this stage, the companies that can define and own THE PROBLEM THEY SOLVE, all its ramifications and the components of the solution have much greater odds of making it to the ‘DOMINATE’ phase.
Most, however, won’t do this.
Which means, as Gartner puts it: While some businesses will achieve tremendous success and growth with AI, the vast majority will fail to unlock its transformative power and will potentially go bankrupt in the process.
The some that make it are the ones who take control and design their categories - the ones who recognize the biggest categories grow on the backs of biggest problems.
2. I’ll Tell You What Problem!
Two companies reckoning with the AI problem desert (™ 2024, Play Bigger all rights reserved) and coming out on top are Ray and Domino. How? By solving actual problems that cost businesses actual dollars:
You Can’t Keep Up If You Can’t Scale …
If your grandfather worked in enterprise tech in this era of AI insanity, it’s a good bet he’d be roaming the halls muttering, “things just don’t scale like they used to.”
And he’d have a point. In an age when enterprises are under increasing pressure to adopt real AI-powered solutions into their offerings, most lack the resources and highly specialized expertise to actually get that done. Trying to bring the old-world enterprise tech playbook to the new world of AI simply doesn’t compute.
That’s the problemAnyscaleis solving withRay, a different kind of computing framework for a fundamentally new computing need. The idea is to make AI’s potential approachable for every organization, by abstracting away the complexity and allowing developers to operate their AI workloads - data processing, model training and model serving - without needing a PHD in compute infrastructure.
Which is a tangible solution, to a real problem, that costs real dollars, and it’s already seeing real traction in the market. Tune into theRay Summit 2024on Sept 30th -Oct 2nd to learn more about how this foundational tech is unlocking the full potential of AI for everyone.
Domino Data Lab Ushers in The Model Era
Another AI company distinguishing itself by solving, you know, real problems is Domino Data Lab- a company that in a 2018 WSJ OpEd predicted AI “Models Will Rule The World.”
The only problem: most enterprises aren’t ready for this new world order. Like Anyscale with Ray, Domino Data Lab recognized a whole host of barriers companies would face in moving from a software-driven to a model-driven economy.
Everything from outdated infrastructure mentality and siloed knowledge to issues with cross-functional collaboration, and landing projects in the last mile - if you can’t do these things as an organization, you can’t compete in the model era.
And as we all know, the costs of that are compounding every day. Which is why Domino Data Lab continues to set the pace in Enterprise AI platforms.
3.Cheaters Never Win Dominate the Hell out of their Category
One of our favorite success stories at Play Bigger is about Qualtricsand the creation of the Experience Management category. There are a million reasons for this, but a critical one: Qualtrics was in a dead heat with a company called Medalia before their category Lightning Strike in 2017; each had identical $1 billion valuations. By the time of their 2021 IPO, Qualtrics was worth 5x Medalia at $21.66 billion, on their way to a near $28 billion high.
Here’s the really interesting part: after the initial Lightning Strike, Medalia quickly mirrored Qualtrics’ language - positioning itself as an Experience Management Company. But by following Qualtrics, Medalia - perhaps unwittingly - helped cement Qualtrics’s position as the XM category leader lending credibility to both the category and its creators.
Now, an even more dramatic category dynamic is at play in the AI space, as Patrick Kulp at Tech Brew reports.AI Washing is rampant in the tech industry, with company after company claiming to have “proprietary AI engines” powering their products, when in reality, they may have just “spent some time futzing around with ChatGPT.” Not only do these “exaggerated claims leave customers feeling very disillusioned by the technologies,” but in a growing number of cases, they amount to “new school fraud,” and charges from the SEC.
Even if they aren’t charged, the failure of these companies to deliver on the promise of the category bolsters the kings and queens as the leaders best equipped to solve the category problem. Rather than de-position the leaders, these pretenders prove the category queens’ dominance. Everyone else is just trying to catch up, or even worse, claiming to be something they are not.
Share Bigger
Category Design ain’t for everyone, but if you know someone with a penchant for thinking bigger, why not invite them to the Play Bigger newsletter?
Last month we debuted Category Creation Lab, and this week we’re happy to share our second episode! Join partners Jason and Mike as they explore continuous category design and why leading on a rocketship isn’t everything it’s cracked up to be when that ship’s crashing back to earth - we’re number one, we’re number … uh-oh.
In category design, like investing, it can make sense to buy the dips - that is, when the value of that hot new thing starts to come into question, there’s a fleeting opportunity to define the problem it solves and engineer the blueprint it must follow to do that. This is category design 101, and it’s why many emerging tech vendors fall off when this happens, while a few go on to enormous success. This is that moment for Gen AI, along with many other high-potential, ill-defined categories. Grab the opportunity. Design the category. Create something legendary.