In any initial conversation with a founder, my least favorite term in startup “lingo” comes up: Product–Market Fit. It suggests some clearly defined inflection point: once achieved, the founder’s journey will get easier. Everyone is looking for the “magic bullet,” which, I never get tired of explaining, does not exist.

The latest candidate for the role of “magic bullet”, Generative AI, holds a lot of promise and gets hyped as a panacea in all corners of the internet. There is no doubt that we have been handed a powerful tool. So what are the implications for startups looking to solve complex challenges such as product–market fit? Figuring out whether there is a need for and interest in your product in any given market is a key part of your journey as a founder. Any shortcuts could give you a much–needed edge over your competition. While AI – spoiler alert – is no “magic bullet”, it might be able to help you save lots of time and effort while you “do the work”, and in the startup world that is huge.

Generative AI is the latest edition of the AI hype cycle with numerous self–proclaimed experts on social media, many recently having pivoted from being Web3 or crypto specialists, predicting how this will reshape how we do almost anything. Except this time, it seems the hype might be real. The last six months since the public release of ChatGPT have seemingly compressed time, achieving AGI (Artificial General Intelligence), meaning sentient machines, now seems a distinct possibility within our lifetimes.

What is Generative AI?

Generative AI is a subset of artificial intelligence techniques that can generate new high–quality content, such as images, text, or even entire virtual environments based on the learnings from large datasets.

I have always been fascinated by AI and its potential, first when I programmed Expert Systems in COBOL as a college student and, as most others in the AI cosmos, I had never expected what has happened over the last few months: Large Language Models (LLMs), used by ChatGPT (Open AI) or Bard (Google), have surprised everyone with their capabilities, seemingly out of nowhere, despite having been years in the making. And many other tools are now emerging, either using the same libraries for training their systems, building on top of OpenAI’s and other APIs, or using their own data to train and build their own AI.

So, to find an easy solution to the product–market fit question, I asked ChatGPT and Bard, if they could help:

Here is what ChatGPT tells us (details after most bullet points cut for brevity, the bullet points are self–explanatory):

“Generative AI can be used to help find product–market fit in several ways: Idea generation: Generative AI can be used to generate new product ideas and concepts based on user inputs, market research, and other data sources. This can help startups identify new opportunities and potential markets that they may not have otherwise considered. <Also> Prototyping, User feedback analysis, Persona creation, Predictive analytics.

Overall, generative AI can provide startups with valuable insights and data–driven recommendations that can help them find product–market fit and achieve success. By leveraging generative AI in the product development and marketing process, startups can increase their chances of developing products that meet the needs of their target audience and resonate with them.”

Pretty good and Bard also gives us some company examples:

“Here are some examples of how generative AI is being used by startups to find product–market fit:

Cure, Gong, Canva and Grammarly.”

So why do startups struggle to find product-market fit?

ChatGPT tells us:

“Lack of market research, Lack of focus, Insufficient customer feedback, Failure to iterate. Competition, Limited resources.

Overall, finding product-market fit can be a challenging process for startups. By addressing these challenges and adopting best practices, startups can increase their chances of finding product-market fit and achieving success.”

Bard adds a key statement in the beginning:

“They build something that no one wants. This is the most common reason why startups fail. They build a product or service that no one wants or needs.”

And if that seems unlikely…no, it happens all the time…

I liked the final statement which goes into the “captain obvious” category:

“Finding product-market fit is one of the most important things that a startup can do. If you can find product-market fit, you’re well on your way to success.”

Duh…

How do startups solve this? I particularly like this suggestion:

“Seek mentorship and guidance: Startups can seek mentorship and guidance from experienced entrepreneurs, industry experts, and accelerator programs. This can provide startups with valuable insights and feedback that can help them improve their product and increase the chances of finding product-market fit.”

So where does all of this leave us? Is AI the answer to all startup challenges? Is product-market fit now virtually guaranteed and on autopilot thanks to AI’s directions?

I am afraid not. Going back to my expert systems days, I would say the answers above do not add a lot of new ideas or concepts. An expert system is essentially a pre- programmed knowledge base that delivers answers and works based on decision trees. ChatGPT’s and Bard’s answers are what I would have expected would be entered in the knowledge base, no additional “AI Magic” required.

Ok, then let’s just ask the question directly and see, if there is any magic:

“What is the “magic bullet” for product-market fit?”

“There is no single “magic bullet” for achieving product-market fit. Product-market fit is a complex and iterative process that requires ongoing research, testing, and refinement. It involves understanding the needs and preferences of the target market, developing a product that meets those needs, and refining the product based on feedback and data. However, there are several best practices…”

As you can see from these examples, there is no magic bullet. Venture capital firm a16z confirms that as part of company-market annealing, which they describe as the effort needed to integrate both the company and the market, “finding product-market fit (PMF) is only part of the journey.” They go on that “most companies don’t find a magical product that just happens to address a key pain point in the market, thus making their go-to-market (GTM) plan a straightforward one.”

It is still the day-to-day work that makes all the difference. This also requires being honest with yourself in assessing whether you are making tangible progress. Are you further along on your journey to product-market fit this Monday than last Monday? Are you REALLY prioritizing, which means giving up good opportunities to pursue great ones? Are you actually productive vs. tackling daily tasks that make you feel productive? If the answer to these questions is yes: great. I am glad ChatGPT confirmed what we have been telling our startups for years resulting in 5 unicorns and $15.6B+ raised.

So, what CAN Generative AI now do for startups and indeed MUST do: It’s an amazing productivity tool, a more sophisticated search tool (I could have found the answers above on Google, clicking through to lean startup summaries, etc. but it would have meant some extra clicks and summaries and time spent) and of course, it can automate workflows; AutoGPT takes that to the next level. Check out some Twitter threads with AI hacks, but please do not fall for the hype.

For further guidance, apply to one of our programs. Our global AI programs not only support AI startups but also serves as a resource for all our other startups and can help you figure out how to use new generative AI tools to accelerate your journey. One day, you might just have to enter a prompt asking for your product-market fit answer and the result will be delivered to you. In the meantime, we are here to help you win a game where the initial odds are stacked against you. As you know, the prize is huge and the need to solve hard problems (think climate crisis) is as great as ever.

About the Author:

Christian was born and raised in Munich, Germany, and started his career at the German media company Bertelsmann in New York City in the ’90s. He made the company’s first investments in Silicon Valley, joining the Board of Rocket Science Games, a company founded by Steve Blank (today famous for “The Lean Startup” method), starting his long relationship with entrepreneurship and forging lifelong friendships within the Silicon Valley startup ecosystem.

Christian has since founded several startups, been part of the leadership team taking Imax Corp. public, and served as Head of Digital for Universal Music Group East Coast as well as Head of Media at GLG.

Over the last years he has focused on helping international founders find success for their companies in the U.S. market. He is currently Managing Partner for the U.S. program of German Accelerator.