The Spectacular Crash and Pivot of a Fintech Star

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In today’s email:

• The Spectacular Crash and Pivot of a Fintech Star: Maybe´s Lessons in Adapting to the Market

• What are the new MOATS for your startup in the Generative AI era?

• Applications Now Open for the Mexico City Founder Institute

The Spectacular Crash and Pivot of a Fintech Star: Maybe´s Lessons in Adapting to the Market

Hey there, my friend,

I recently stumbled upon a story that reminded me of a golden rule in the business world: it's not always about having the best product or the most personalized service. It's about understanding the market, being adaptable, and knowing when to pivot.

Remember the fintech app, Maybe? It was born in early 2021, a time when the stock market was booming and the crypto world was making it rain virtual dollars. The concept was intriguing - a net worth tool that promises consistent data across various financial sources. It seemed like the perfect time for a financial tech start-up, and Maybe successfully raised an impressive $1.45 million in a pre-seed round from over 1,300 investors.

But, as many of you who have dabbled in the B2C fintech space will know, the reality was far more complex than initially anticipated. After 18 grueling months of development and significant changes in the market dynamics, the team finally launched the product. Despite their best efforts and an impressive piece of software, it fell flat. The product was over-engineered, and the team had focused too much on data-source quirks and creating a pretty dashboard instead of providing actionable advice and information.

In the end, six weeks post-launch, they only had 50 paying customers, out of a waiting list of more than 10,000 people. This was a stark reminder that even with a great tech product if the fundamentals are off, failure is a potential outcome. The team found themselves in a precarious situation, without the financial runway to support their burn and too much legacy code to rework the product​^.

So what do you do when you face such a scenario? For Maybe, they hit the reset button. The team slimmed down to three people, and they pivoted their idea to a virtual financial assistant that could answer questions about personal finance and investment. But yet again, the feedback was lukewarm. The tool was more novelty than utility, and the harsh reality was that most people just don’t have financial questions more than a couple of times a year​^.

This led to some tough team conversations. They found themselves at a crossroads, with the options of selling off the company assets and returning a fraction of the investments, or taking one last swing for the fences and pivoting to a completely different market.

In the end, they decided to pivot. It was a bold move, but it serves as a stark reminder of an important business lesson: technology isn't always the key to success, and knowing when to pivot could be the difference between the life and death of a company.

So, my friend, as we navigate our business journeys, let's remember to stay flexible, be realistic about our offerings, and most importantly, listen to the market. And remember, it's okay to pivot. Sometimes, it's the best move you can make.

You can find out about Josh Pigford, the Maybe founder here!

So, after diving deep into the story of Maybe and its pivot in the face of adversity, what's your perspective on it? Do you see it as a cautionary tale warning of the pitfalls of over-engineering and lack of market adaptability in the fintech world, or do you view it as an inspiring journey of resilience and the power of strategic pivots in a volatile market?

What are the new MOATS for your startup in the Generative AI era?

As an entrepreneur in this rapidly evolving digital age, I find myself constantly grappling with the concept of building and maintaining "moats" around my startup. A term popularized by Warren Buffett, 'moats' refer to competitive advantages that create barriers to entry for competitors, thus protecting a company's market share. The article "The New New Moats" by Jerry Chen from Greylock provides some enlightening insights on this topic that I would like to reflect upon^.

The original concept of "The New Moats" as introduced by Jerry Chen in 2017 highlighted the importance of building defensible business models by using AI and data, which he referred to as "Systems of Intelligence". He argued that these would become the new moats, replacing traditional economic moats such as economies of scale, network effects, proprietary technology, high switching costs, and brand loyalty.

In 2023, Chen revisited this framework to reflect on changes in the AI landscape over the past six years. He pointed out how advancements in AI, specifically large language models like GPT-4, PaLM2, and LlaMA, have been game-changers, making AI the enabling technology of the moment. Amid this explosion of AI startups, he revisited his original question: "What are the new moats?"​

In the past, traditional economic moats were widely recognized and used by technology companies. Some of these include economies of scale, network effects, deep technology or IP, high switching costs, and brand and customer loyalty. Companies such as Microsoft, Google, and Facebook have all built strong moats based on economies of scale and network effects, thus fortifying their position in the market.

However, given the dramatic shifts we are experiencing in technology, including the move to cloud-based applications and the integration of AI and data, many existing moats are being rendered useless. For example, open source tools and cloud technology have shifted power to the 'new incumbents' such as Apple, Facebook, Google, Amazon, and Salesforce. It feels like every advantage a startup may build can be replicated by another team, or at the very least, that moats can only be built at massive scale.

But that's not to say that moats are a thing of the past. I believe that deep technology moats aren't completely gone and defensible business models can still be built around IP. This involves identifying a place in the technology stack and becoming the best at it. This means choosing a technical problem with few substitutes, one that requires hard engineering and operational knowledge to scale.

Today, the market is favoring "full stack" companies, where technology is becoming an invisible component of a complete solution. As an entrepreneur, it's important for me to realize that while technology is crucial, it's the customer problem and the complete solution to that problem that truly matters.

As I delve into the heart of this narrative, I find myself musing over several key insights that might shape my next startup venture.

1. Unleashing Data Power: The real treasure isn't simply in accumulating heaps of data, it's about effectively mining that treasure trove. It's about creating savvy products capable of wielding this data to solve problems, streamline workflows, or even invent new ones. The magic really starts when you're able to enhance customer upselling, offer automated support, retain employees, or flag security anomalies, all by intelligently harnessing data.

2. Carving a Niche: We're talking about software solutions that aren't just brilliant—they're tailor-made for specific industries like healthcare or financial services. This industry-focused lens adds a level of understanding and specificity that can deepen your competitive moat, helping you stand out from the crowd. You see, when your solutions can automate or even replace an entire enterprise workflow, you're providing immense value that's hard to beat.

3. David vs Goliath: Remember the likes of Salesforce and SAP? These software giants, once deemed invincible, are not as invulnerable as we thought. Their extensive dominance is being challenged by nimbler, more innovative players. As a startup founder, taking on these behemoths might seem like a tough gig, but remember, it's all about the strategic use of data and AI. Challenge established norms, disrupt the usual workflows, and you might just hit the jackpot!

4. The AI and ML Revolution: Alright, I'll be the first to admit, we're all a tad weary of the AI buzzword. But beneath the hype and jargon, the transformative power of AI is undeniable. Pairing Machine Learning (ML) with business processes and data opens the door to a brand new world—a world I like to call the "intelligence system". And the fun doesn't stop there. With evolving AI techniques such as neural networks, we're only scratching the surface of what these future applications might accomplish. Remember Google's ad optimization with ML? It's only the beginning.

As I navigate this journey, I look forward to revisiting this concept and adapting my strategies as necessary. After all, in this dynamic world of startups, staying agile and open to change is the key to success.

You can read the full article from Greylock here.

Get advice from YC

Guess what? It's finally time for us to test out this ChatGPT bot that's been loaded with all the wisdom from Y Combinator. Honestly, it's more than just a bit of fun to toy around with - it's also an excellent source of inspiration.

Think about it, we can learn about the types of tools we could create using large language models, all tailored to specific knowledge domains. It's pretty exciting, isn't it? So, let's dive in and see what this bad boy can do!

I'd love to hear your feedback on today's newsletter! Is there a specific type of content you'd like to see more of in the future? Since I'll be releasing a new edition each week, I welcome any suggestions or requests you may have. Looking forward to hearing your thoughts!

The Menu Magic is written by Francisco Cordoba, a Fintech entrepreneur living in London. MIT Innovator, Edmund Hillary Fellow, St. Gallen Leader of Tomorrow. Erasmus Mundus scholar. BA in Business. Zinc VC Entrepreneur in Residence.

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