Arthur C. Clark once said that “any sufficiently advanced technology is indistinguishable from magic.” The magic-like experiences each of us have felt using AI over the past few years help explain why so many startups have found instant Product Market Fit. Today’s AI products are so magical you just can’t help but want to use them. ChatGPT is the fastest growing consumer product ever. And public launches of other AI-native products like Waymo, Suno, Midjourney, and perhaps even Google’s new Notebook LM podcast feature are other recent examples of instant Product Market Fit. As one AI founder recently said to me: “it doesn’t make sense for us to launch the product until we reach a technical breakthrough, because the breakthrough is the product.”
This isn’t your typical Product Market Fit; instead, it’s what I call Magic Market Fit: when a technological breakthrough is so magical that it instantly meets a market demand. Unlike Product Market Fit (PMF), which typically requires months or even years of experimentation and constant iteration across product, design, engineering, and go-to market efforts, Magic Market Fit is met the moment a technical breakthrough is achieved. ChatGPT is a great example of this phenomenon. The design and user experience is ridiculously simple – it’s a basic chat interface. On the other hand, the technology underneath it is anything but simple, and the results are so impressive that people can’t help but be drawn to it.
In the absence of technology, startups needed to hack their way to PMF through product and design.
The traditional search for PMF looks quite different from what we’re seeing today with AI, and products built during the mobile revolution of the 2010s serve as a good example of this distinction. After the launch of the iPhone and Android platforms, builders of early mobile apps had to navigate an evolving landscape supported by hardware, software, and infrastructure that was far from robust in the early days. It’s easy to forget how many of the iOS and Android features and services we all utilize today didn’t exist in the early days of smartphones. And hardware features like smartphone GPS and cameras were only just becoming available in the early 2010s. Plus, the lack of cloud computing, lightning fast internet speeds, and longer battery life hindered the capabilities of startups and their products. Of course, all of these things quickly came online over the years that followed, and teams raced to adopt them as a result. But it took time, grit, relentless adaptation, and product hacks for startups to break through, and all of the above contributed to teams needing to iterate their way to PMF.
Fast forward to 2024, and the infrastructure exists for new products to be quickly built and distributed to many millions of people. The challenge now, however, is that the market is extremely crowded and there’s already an existing market leader established for nearly every category. Just think about how many times you’ve seen a new mobile app launch and you’ve thought to yourself, “haven’t I seen a dozen different versions of this app?” But this ability to distribute your product to millions with the flip of a switch also partially explains why AI products can find Magic Market Fit today. Building products has become easy. But building magic? That’s special. And that’s exactly why customers are instantly drawn to AI products.
Tech iteration is the new product iteration.
Beyond this now-mature infrastructure which enables developers to quickly and cheaply build and deploy products, there are other major factors contributing to the rise of Magic Market Fit. First, the exponential growth in computing power, driven by advanced GPUs, has made it possible to train deep learning models at unprecedented scale, which would have seemed impossible only a few years ago. Plus, the availability of huge data troves from the internet, social media, and many of the products built during the mobile revolution, serve as the fuel needed for AI products to learn and perform. And lastly, large investments in AI research and talent from both the world’s biggest companies and startups alike has created huge demand for teams to translate their tech breakthroughs into market-ready products. All of these factors have converged to create a unique moment in time where magical technologies like ChatGPT, Midjourney, and GitHub Copilot can achieve Magic Market Fit once a new technology is unlocked.
With the search for Magic Market Fit driving roadmaps and hiring plans, perhaps it’s no surprise that startups look differently than they did only a few short years ago. Teams are forgoing hiring extra PMs and sales leaders in favor of doubling down on engineering and research, hoping to mine magic faster than their competitors. Even designers are becoming deprioritized in favor of “engineers who also know Figma.” But it’s hard to know if this trend will last. If history rhymes, and this AI market crowds just as quickly as the mobile market did, we may see things rotate back in the other direction, especially as engineering – and potentially AI research – becomes easier for less technical teams.
In the meantime, we should expect the pace of change – and investment – to accelerate towards a future where what really matters isn’t great product; it’s magic.