AI is continuing to transform our lives, and not a day goes by without a new announcement, a new investment, or a new model being released. But who will be the long term winners in this field?
If we look back to what was the dawn of the e-commerce era in the nineties, a similar pace of investment and innovation was taking place during the internet boom, and the ultimate winners were not easy to predict.
In December 1997 Amazon had a market cap of $800 million, and eBay had not yet gone public. By December 1998 Amazon was up more than 20x at $19 billion, and eBay, fresh off its IPO traded at $11 billion. As another year went by and the millennium drew to a close in 1999, Amazon traded at $270 billion vs $17 billion for eBay.
Then the dot com bubble burst. By December 2003, Amazon was trading at $11bn and eBay had surpassed it (driven by a more profit oriented performance) to $21bn. Roll forward to more than two decades later, as of April 2025, Amazon is now at $2 trillion vs. $32 billion for eBay.
If we look at other big technological innovation, for example smartphones, similar patterns emerge. In 2007, Nokia’s market cap was $117 billion, larger than that of Apple at $112 billion. Today, Apple is at about $3.3 trillion vs. $29 billion for Nokia, and very few people would have heard about the Symbian smartphone Nokia was launching in 2007. Another aspiring smartphone maker, Palm, traded at a peak valuation of $53 billion, but today many people have never seen a Palm Pilot, much less the Treo that Palm launched in 2003 as a smartphone.
So let’s switch back to AI. Who will be the dominant players of tomorrow? Given the above, it may not be easy to predict.
In simple terms, the inputs needed to win are vast amounts of data and power (both computational and electrical) along with distribution channels. This requirement already gives certain existing players a big advantage.
Tech giants such as Alphabet and X have access to vast amounts of proprietary data in the form of all our e-mails and social media content. With these existing user bases, distribution is also easier. Advantage incumbents in this case.
Yes, ChatGPT has a solid first mover advantage, but it will likely have to work harder to overcome the data edge of Gemini and Grok. Is this another eBay vs. Amazon moment?
Furthermore, the sophistication and scale of AI models are escalating rapidly, directly impacting who can compete. Today’s leading models—like OpenAI’s GPT-4o, Google’s Gemini 2.0, or xAI’s Grok 3—boast parameter counts in the hundreds of billions, requiring massive datasets and compute resources to train. For instance, training a model like GPT-3 (175 billion parameters) reportedly took 3.14 × 10²³ FLOPs (floating-point operations), a number that’s ballooned with successors. Again, advantage incumbents.
In the world of fintech, banking giants such as JPMorgan and Capital One also have access to decades worth of proprietary user data. Likewise, they have a distribution advantage, but they will have to fight the “compliance says no” mindset in bringing innovation forward, a story we are familiar with as fintech investors at QED. Advantage incumbents, as long as they don’t drown in their own red tape. Most certainly an exciting opening for aspiring challengers.
All these examples are from the United States, but looking through a geopolitical lens, we see the advantage China also has. In addition to the vast amounts of data generated by its population it is also the biggest solar panel producer globally which gives them an advantage on the power dimension – the marginal cost of solar power is close to zero (as low as $0.01/kWh). China currently produces about 80% of solar panels globally, plenty to power future GPU farms.
Europe, on the other hand, has been a laggard. Using regulation to try to even the playing field will be tempting, so we can expect an amplification of the debate around who owns user data and GDPR. This is an important debate to have, but not at the expense of stifling innovation. And there is strong impetus around European domiciled models, and many exciting startups and scaleups, so expect more in this space. Europe cannot afford to miss the boat on AI.
This also highlights another incumbent with access to both data and power: national governments and their various branches, including the armed services. As the importance of data and AI becomes clearer, there will be a temptation for many governments to collect and store more data on its citizens, and then use that data to power AI models. To ponder the possible social and political implications of this, the book Nexus by Yuval Noah Harari is a good starting point.
So while it looks like the incumbents look to lead the AI race with data and power, we are VCs and exist on the premise of disruption and the power of innovation. AI creates plenty of opportunity, especially in fintech where regulation is difficult to navigate for many.
The so what for fintech entrepreneurs building in the AI space is this:
First, find your own data edge and grow it and guard it jealously. If your business model gives you proprietary data, this is your doorway into a future AI moat. Use it wisely.
Second, regulation is more cumbersome for giants. Use regulatory arbitrage to your advantage while you can. Be nimble and take calculated risks. You need to understand regulation, yet not be hamstrung by it.
Thirdly, look to niche areas to grow from. Amazon started as a bookseller and became a global e-commerce giant. Fintech presents an interesting wedge for founders, where niche data sets can be sharpened to provide an edge to a targeted user base with domain specific models. Think fraud detection, the HR channel and the future of work, auto finance and insurance, tax and tariff complexities, and many others.
From there, be the best operator out there and out-maneuver your competition, adding one new vertical or market at a time. And QED is here, ready to help with our global reach and understanding of regulatory businesses.
Finally, look for where AI is disrupting incumbents. Regulation slowing down banks, making them laggards in deploying AI may be one example. Another area may be in distribution, and the emergence of voice as a distribution channel. We often say that the smart phone is a very clunky input/output channel with a small screen and small keyboard for our two thumbs. Being able to ditch your screen and talk to your AI, having it summarize your inbox, reply on your behalf etc. may supplement our use of smartphones. More on that in our next blog.
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