How AI will Transform Fintech: Cross-border SMB Commerce Rails

Today the cross-border logistics plus payments stack is broken for merchants under $10-20 mn of revenue.

Much like sewing together a patchwork quilt from scratch, these merchants today have to stitch together Shopify, ShipBob, UPS, Avalara, Wise, local import brokers, customs paperwork, random tax agents, and then sit down and pray nothing gets stuck at some border.

Economically speaking, landed cost miscalculation is also painful: overcharging kills conversion for merchants, while undercharging eats up margins. Trying to do this yourself eats up valuable time.

Customs classifications are a black box, HS codes are arcane, and the wrong code once again costs time and money.

Returns are pure chaos, and even something as simple as understanding and optimizing FX spreads is daunting for merchants.

And if you are an SMB, bigger players like the shippers or Global-E don’t really pay too much attention to you.

An AI driven platform can change all this, and Swap Commerce, where we led the Series-A is already tackling this with great success.

The vision for Swap is clear:

A real time accurate landed cost calculation (including duties, VAT, brokerage fees, etc.) delivered in milliseconds during checkout
Customs and compliance automation with the correct import/export declarations and registrations handled smoothly and quickly
Payments and FX bundled, also offering local payments methods, even acting as the merchant’s cross-border PSP
Orchestrate all logistics including shipping labels, duties paid options, returns routing, and warehouse integrations
A returns clearinghouse that can offer in-country consolidation hubs and allow cheaper returns
Localizing pricing, currency and tax display on a country-by-country basis
Fraud and risk management by tracking cross-border specific false positives and false negatives a traditional PSP may miss.

The regulatory volatility today, starting from tariffs and stretching back to Brexit all have created an even more urgent need to solve this pain point. And AI is making compliance automation solvable in a way it had never been up to now.

So, we are understandably very excited about the prospects for Swap. As we continue to scale up, what appears to be a workflow tool also reveals crucial network effects. HS classification training sets, customs and return data, lane-level logistics performance data all give Swap an edge that gets even stronger as it scales.

We will look at other AI driven fintech opportunities in future blogs, and analyze some of the common themes that emerge from these opportunities, but by ways of a spoiler alert, Swap is an example of two very salient themes that are emerging in fintech AI:

Combining the power of AI with a deep understanding of regulation & compliance, and
Using AI, automation, payments rails etc. to present a bundled solution to the customer as opposed to just the individual components of software or automation or payments.

Another way to think of the above is that the fintech winners of tomorrow will combine AI & automation, an understanding of regulation & compliance, and embedded finance into one solution. We can think of this as Solution-as-a-Service, and the companies, like Swap, that deliver the best will be the winners.

In future blogs we will look at how these themes will transform other areas such as AI driven treasury ops, real-time compliance oversight at regulated entities, AI driven NPL solutions, and many more.

Best Practices in Fintech AI – Notes from our CEO conference

We recently had our 17^th Annual QED CEO Summit in Washington, D.C. and had an amazing turnout from what we (admittedly somewhat biasedly!) think are the most exciting entrepreneurs and builders in the world of global fintech.

Given the times we live in, it should not come as a surprise that we had lots of great discussions around AI and how startups and scaleups are using it across several functions. In the spirit of continuing this very interesting (and certainly fast evolving) conversation, we wanted to share some of the key takeaways from these sessions.

I. AI is being used by all, but to varying degrees

One not so surprising insight was that when we asked CEOs to rate how much they have integrated AI to their organization on a scale of one to ten, nobody thought they were even close to a ten. More interestingly however, as CEOs listened to the practices of other peers, some changed their own rating from high to low single digits. The twofold takeaway here is that one the variance across organizations is indeed wide, and two that cross fertilization and sharing notes with others brings much improved awareness. So keep sharing, and keep learning – fast!

II. Data & prompt engineering: Common frameworks are emerging on agent workflows

Several teams are converging on various prompt-engineering frameworks (e.g., a simple one being Role-Context-Task-Format). But consistency varies widely across companies, and we’re still in the early phase of codifying ‘how we work with agents’ and the norms are evolving very quickly.

This then leads to a key skill set around writing prompts, or in other words “managing agents” developing rapidly and becoming very valuable.

In this fast evolving world, it is also quite common to use one agent to write a prompt for another agent, for example using ChatGPT to write a prompt for Claude.

While there were many examples of note here and lots of best practices, things are changing so quickly, that the most salient point was the emergence of a new “AI culture” where speed of adaptation is becoming a defining characteristic.

III. Agent + human workflows: A new sort of team

Workflows are changing very fast also, and it is clear that all CEOs now think of their teams as being made up of humans plus agents. Most CEOs are finding ways to incorporate agents into their teams and workflows in creative ways, where each one supplements the other.

One interesting example that was shared in the financial infrastructure industry involved an AI agent joining a customer product discovery call, and then based on the notes from that call writing prompts for another AI agent to create new wireframes for the new product that was being discussed. According to this CEO, just using this simple process cut down on developer needs by a factor of five when measured in manhours over a period of two months, and also enabled one product manager to cover three times as many releases in one quarter.

IV. Specific issues around AI in fintech

In the context of fintech, one very important external interaction to manage was around regulators. Most fintech CEOs were aware that regulators would likely be slow to adapt, and there were very valid concerns the community would need to answer around model drift, fairness, explainability, and data lineage with regulators. One best practice that was brought up here was to share data and insights with regulators in an open manner, bringing them along on the journey.

V. HR impact is emerging as not everybody can keep pace

A concrete example is that while common frameworks on how to generate the most effective prompts most efficiently are quickly emerging, not everybody in the organization is able to keep up. As a result, there is certainly an issue around what to do with those in an organization that are not able to adapt to this new world at equal speed.

The not so surprising upshot of this is that organizations are parting ways with what we may refer to as AI luddites (not an ideal outcome for anyone to say the least), but also a lot of thought is going into how to upskill the workforce to adapt to this new world, and how to incentivize everybody to learn and adapt their mindset.

The importance of adapting the firm’s culture to AI becomes very important as a result, creating best practices around how AI is used, driving adaption, and finding ways to share insights quickly.

VI. A new type of tech debt: some words of caution

One interesting point was also made around AI generated product prototypes creating a new sort of tech debt or friction in the product discovery and design process. The issue here was that a simple product prototype created by an AI (as in the example above) may not necessarily be easy to productionize due to a host of technical issues. The best practice shared in this case was to treat the AI created demo as a “throwaway prototype” or a PoC and then start from scratch (or at least give the team latitude to make the needed layout changes) later in the development process.

VII. Managing interactions with the outside world:

What also became clear is that while managing agents and their interactions with humans was a new and emerging skill set, managing their interactions with the outside world was another.

One example of this was an interesting theme around B2B focused businesses finding ways to educate their business customers on how to use AI in implementation. While managing internal culture and adaption was one thing, managing the customer’s adaption was another altogether. An example brought up here by one CEO as that for each dollar spent on AI development, they were budgeting one dollar for helping their customers with adaption.

VIII. Sharing best practices is empowering

A key insight is that CEOs loved the sharing of insights enabled by our conference.

In this spirit of sharing, if you found this interesting also make sure to refer to the additional content put out by the QED team around this topic for much more, including signing up to our newsletters and this blog. And most importantly, as you continue building your fintech business, do reach out to our global team – we’d love to hear your thoughts and incorporate your perspectives, especially around how you are using AI to compete – and win.

The Coming Stablecoin Regime — and What It Means for Fintech

Hot on the heels of the U.S. GENIUS Act, the Bank of England Governor Andrew Bailey published a very clear and concise framework in the FT entitled “The New Stablecoin Regime”.

As more regulation is clearly coming for stablecoins, all market participants including entrepreneurs need to be aware of the risks and opportunities created by this coming wave.

Let’s therefore first look at the similarities and differences between Bailey’s approach versus the GENIUS Act, to then better understand the associated risks and opportunities.

Firstly, the similarities.

1. Stablecoins need 1:1 backing and peg stability: Goal here is to eliminate the risk of “breaking the peg” and ensure the holders can always redeem at par.

2. Payment instruments, not investments: Stablecoins are a medium of exchange only, unlike crypto assets that could be regulated as investments.

3. Consumer protection and speedy failure resolution: The goal here is to make stablecoin users as protected as bank depositors (or at least close to it).

4. Regulatory oversight and licensing by ways of formal authorization and ongoing supervision: If you issue something that functions like money, you need to be regulated as a financial institution.

As for the differences:

1. BoE is more strict on backing assets (eliminating credit, rate, and FX risk), with the GENIUS Act leaving it at 1:1 backing by high quality reserves.

2. Resolution and Insurance: US emphasizes disclosures and anti-fraud and giving stablecoin holders priority in an insolvency, whereas BoE goes further and proposes a statutory resolution regime plus insurance scheme.

3. The US sees it more as a parallel payments system outside traditional banking yet under financial supervision, whereas BoE sees a closer integration with central-bank infrastructure, possibly even giving systemic issuers access to BoE reserves to ensure full convertibility.

4. The GENIUS Act emphasizes speed, with implementation and detailed rules coming over 18 months. The BoE approach is slower and more prescriptive.

5. BoE is also more silent on stablecoins bearing any sort of interest, whereas the U.S. framework explicitly prevents this.

Overall, the U.S. model is more pragmatic and market led, emphasizing innovation on the margin, whereas the U.K. model is more precautionary and central bank centric, focused more on systemic risk.

The regulation will clearly impose a cost and compliance burden on current market participants, but it will also bring with it increased legitimacy, more widespread adoption, and ideally, greater long term stability.

Another very notable opportunity that this creates for market participants and entrepreneurs is the first step towards fundamentally transforming the future of banking.

Beyond compliance, these regulatory shifts hint at something far deeper — a structural transformation in how money and credit interact.

Historically, money creation and lending went hand in hand under traditional banks. With fractional reserve banking, banks created money out of thin air (you can read my blog on Why Your Money Does Not Exist for more on this), but in return for this literal license to print money, banks were also expected to lend to boost economic growth with lending.

If the creation of money via stablecoins becomes disaggregated from traditional banking, a vacuum will open up. Traditional banks may lose cheap funding (deposits) and to maintain return, they or others, will provide credit through off balance sheet vehicles and private credit funds, i.e. the shadow banking system.

Hence, these new regulations will create a twofold opportunity for fintech entrepreneurs: on the one hand they can create new and innovative companies that use stablecoins as a new kind of money that enables commerce across all corners of the world, while on the other hand they can create new modular and tech driven lending companies that further disaggregates what used to take place under the roof of a traditional bank.

Imagine cross-border B2B platforms settling international trade in stablecoins, or modular credit providers offering 21^st century lending solutions using new sources of data such to underwrite in real time.

As entrepreneurs and investors navigate this next chapter, the winners will be those who treat regulation not as constraint but as catalyst — and that’s exactly where we at QED Investors aim to partner and help.

The Agent Store Is Coming. Here’s What That Means.

We’re entering a new frontier — the Agent Store, akin to App Store but filled with autonomous AI assistants. Think of jotting down “travel agent,” “CFO assistant,” or even “model gimp,” and voilà — you browse, install, and transact (or should we say “hire”). No prompts needed, no integration headaches.

Why is it happening?

Developer monetization must evolve. Just as apps needed a marketplace, our GPT + tool ecosystem demands distribution, review, billing, recursion.
User demand for turnkey agents. Most users don’t want to build prompts (and they are actually not that good at it!) — they want to install “deal‑sourcing agent” or “legal memo writer” and then go back to the water cooler conversation.
Infrastructure is ready. With persistent memory, API access, LLMs with tool use, and billing frameworks already live, the missing piece has been distribution. And Microsoft already released an embryonic version in April 2025. Expect many more soon.

What will it look and feel like?

A curated storefront: categories, ratings, developer info, pricing tiers (free, freemium, subscription).
Seamless install: tap, authenticate once, agent begins working across apps.
The emergence of voice: Two way conversations will very likely start taking significant market share from our thumbs as an input/output device, especially on mobile devices.
UX expectations & POAs: sandboxed permissions, digital Powers of Attorney, clarity of capabilities, ease of uninstall — just like mobile apps, but intelligent.
Evolution of autonomy: Simple Powers of Attorney will be replaced with ever increasing autonomy over time.
Transparent billing: platform revenue share, PCI‑compliant subscriptions, reviews and updates managed like iOS/Android.
New managerial skills needed: MBAs will now need to get good at managing teams of non-human agents.

What should existing startups and companies do now?

Build agent‑native not just APIs. Start by layering memory, autonomy, tool orchestration.
Prepare UX and billing flows – be store-ready early. Think onboarding, onboarding-to‑subscription, audits, support.
Differentiate with data: proprietary customer data = AI moat — especially in verticals like fintech, legal, compliance.
Start building distribution partnerships. If OpenAI, Anthropic, or Gemini open a GPT store, early agents gain discoverability.

What should new founders do?

Pick niche, move fast. Build domain‑specific agents where vertical knowledge matters — e.g., K‑1 tax prep, credit underwriting, ESG reporting.
Validate fast with no-code stores like Custom GPTs. Gather feedback and iterate.
Plan revenue share: think subscription tiers, usage fees, white‑label models. Monetization is table‑stakes.
Engage the community. Launch a waiting list, host previews, build a reputation. Early agents likely get featured.

Is it a land‑grab?

Absolutely. The first wave of agent stores will be winner-takes-most. Expect:

Featured position bias: curated picks dominate downloads.
Network effects: as more users flock to certain agents, integration partners follow.
Platform lock‑in: store policies, bundling restrictions, revenue shares define who wins.

Winners will be those that arrive early, deeply verticalize, price smartly, and evolve with platform policy — not just code against GPT-4.

At QED were here helping fintech manage the transition to mobile as companies like Nubank and Braintree spearheaded that evolution. We are very excited to help the next generation of fintech founders think through this exciting platform shift. We are only a call away!

Thoughts on a New Era: From Wheat to Networks

Human society is now rapidly evolving from a services-based economy to a network and intelligence-based economy.

But what does that mean, and what can we expect from the future? The following broad historical view could be helpful in putting current changes into context.

The hunter-gatherer stage of human evolution kicked off as early as 2.5 million years ago with early species such as Homo habilis (when tool use and foraging first emerged). In terms of modern-day humans, we could say that the start was with Homo sapiens around 300,000 years ago. The default mode of production was the use of simple tools to hunt and trap various animals and dig up tubers, crack open hard-shelled fruits etc.

This was the default mode of human existence until 12,000 years ago, when a big technological leap occurred: humans domesticated wheat and other plants. As an interesting side note, looking at Earth from space, an alien may argue it was actually wheat that domesticated humans – if you find this topic interesting a good book to read is Oceans of Grain.

The move to farming was a big leap indeed. As a result, human populations across the world increased from an estimate of 1 – 10 million to about 150 – 300 million by 1 CE. The farming revolution also allowed for excess food production that could now employ people not involved in the production of sustenance and food gathering. Thus armies, kings and queens, priests, and empires entered the rapidly growing world. In many ways, we can say that this was the beginning of history. Think the Mayans, Ancient Greece, and the Roman Empire.

The farming boom lasted about 11,000 years until in around 1760 CE the industrial revolution kicked off in Britain. It started with mechanization (think spinning jenny, the steam engine, etc.) and was fuelled by coal and urbanization. Human population once again turbo charged, going from 770 million globally in 1760 to 2.5 billion in 1950.

The industrial age lasted even shorter, in total less than two hundred years until the mid-20^th century when the services-based economy and the digital age was ushered in. This was characterized by the first computers, the growth in financial markets. There were big population and employment shifts. Just as workers had moved out of farms and into factories in the industrial age, those workers now moved into offices in the services age. Population also ballooned, to 8 billion in 2022.

And what were the factors of production in each of these ages? For hunter-gatherers it was wild resources and human effort with no capital beyond simple tools to speak of. Entrepreneurship just meant taking risk to survive in the literal jungle.

As we moved into farming, wild resources were replaced with cultivated fields, a labour class of farmers and herders emerged, complemented with specialized classes of priests, soldiers and administrators. Capital tools got an upgrade, animals were domesticated. Entrepreneurship started emerging with landlords, traders, and innovations like crop rotation.

In the industrial age, labour continued to specialize even further, with concentrated factories urban centres started growing, and as heavy investment such as factories and infrastructure were needed, capital entered the stage in a big way as a factor of production. It is no surprise that Das Kapital was written during this transformative age. Hand in hand with capital, entrepreneurship also evolved, risk was scaling and robber barons such as Rockefeller, Watt and J.P. Morgan entered history.

In the services age, knowledge workers that used their brains instead of muscles emerged. Capital shifted to include more abstract things such as brands and patents. We can also think of this as the knowledge economy, and human labour was still a limiting factor, but more because of their knowledge and reasoning capacity, not their arms and legs.

We have now entered the network and intelligence age, and the factors of production, as well as the limiting factors on economic growth are once again shifting. On the labour front, humans that used to be knowledge workers in offices are now rapidly moving out, being replaced by computers that can reason faster, more consistently and more accurately.

As a result, on the capital front, the main constraint of growth is shifting from humans to computational power (electricity plus advanced chips). Economic moats are not built around big offices or big factories, but proprietary networks and the data they generate.

The networks of this age are multi-faceted and intertwined. On top of communication networks (satellites and cables) we have financial networks (Visa, MasterCard, Swift), as well as social networks (X and others). These networks generate vast amounts of data that in turn fuels the vast computer intelligence that is evolving ever faster.

This age is also characterized by ever faster innovation and disruption, yet those that control the networks and the computers will yield unprecedented power.

So what does this mean for entrepreneurs working with QED and building in this age? Access to proprietary data and building a network is most certainly the holy grail. If your business does not have strong elements of this, even if in a niche form, you may want to reassess your business plan.

Given that the pace of innovation and disruption is increasing, opportunities for entrepreneurs are also multiplying. Look for incumbents that are hampered by regulation and may be slow to react to the new age.

The skills that are needed in this age are agility, speed, adaptability and calculated risk taking. Taken together, these amount to being anti-fragile – building organizations that emerge stronger from each successive disruption and shock. You will also have to be good at incorporating non-human agents into your org structure. Sounds simple, but laws, regulations, and human nature will complicate it.

Yet capital is still needed. Computing power will not be free, whether from humans or machines. And acquiring customers still costs money. As QED, we are here to help.

Who Will Win in AI: Data, Regulation, and Power

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.

Food, Fashion, Football and Fintech: Top Five Impressions from Milan

Our QED tour of local European fintech ecosystems had started with Istanbul, and we now continue with Milan which Bill Cilluffo and I visited this week in a memorable and impressive trip.

Bill Cilluffo and I trying to pick from a very impressive wine list, aided by Chef Carlo Cracco

It was of course not our first (and very likely not our last!) time there. Bill is Italian American, and a frequent visitor, and for me, it was my third trip to Milan in one month. As the title of this blog implies, there are many reasons beyond fintech to visit this beautiful place. And as QED we have already been in attendance for board meetings as a guest of our friends at Nextalia with whom we are co-investors in ShopCircle.

Impression #1: Italy is a big economy, and the dynamism is increasing rapidly.

To say that Italy is the fourth largest economy in Europe in many ways belies its true size. At 2.5 trillion of GDP, it is about three quarters the size of France, and two thirds the size of the UK, and as these numbers demonstrate, the size difference is not substantial. If we look into areas like consumer goods, the size difference becomes even less.

Duomo di Milano, a beautiful example of Gothic Italian architecture

On top of this, Italy is also making changes to its tax system, making it easier and more advantageous for expats and former emigrants to locate here. It also boosts a very agile and dynamic SME sector, which when combined with fintech innovation and more investment dollars (or we should say euros) will drive growth and innovation.

The Generali building, a beautiful example of modern Italian architecture

Fintech investing is also increasing rapidly, having at times exceeded the billion dollar mark, and is projected to reach 1.5 billion annually by many insiders, which will continue to amplify positive tailwinds.

Impression #2: There is an opportunity for Seed and Series-A focused specialist funds.

The investment ecosystem seems dominated by two ends of the barbell – a very strong network of angels and family offices on the one side, and pan-European as well as global institutional capital going after the bigger deals on the other side.

This dynamic creates somewhat of an opportunity for specialist Seed and Series-A focused funds in this market. Currently this is being served by many pan-European seed funds, but the opportunity is nonetheless there.

A lovely lunch hosted by our friends at Nextalia

As QED, needless to say, we are very happy to partner with all of these players, at any stage of investing.

Impression #3: Bureaucracy and red tape creates huge opportunities for fintech.

Local laws and regulations can seem hard to follow, or even byzantine at times, and while this creates some friction for businesses and consumers, it also creates tremendous opportunities for fintechs.

Whether in proptech, taxes, payroll or any such area, there are pain points to be eliminated and exciting new businesses to be built.

Impression #4: There is no “United States of Europe”

Despite the Eurozone and the customs union, there is no substantial political unity in Europe when compared to other big political entities. As a result, local laws and regulations make it more difficult to scale fintechs across borders in Europe, something that is not as much the case in the U.S. and China.

The best fintechs take advantage of passporting laws and their own ingenuity to overcome this, scaling across borders, and we have many examples of such fintech success stories in our portfolio, such as Payhawk, and another QED investment Klarna that is now present in Italy also.

But the friction created by local regulations give Italian fintechs an edge on their home turf, and this is certainly something the best of them take advantage of to build strong moats.

Impression #5: The best is yet to come

Comparing to a much smaller country like Sweden, there has been no Spotify, Skype, Klarna, or iZettle coming out of Italy yet.

Our good friend Gianluca taking a breather from a board meeting

But there are clear signs of tech and fintech titans in the making. Companies like Bending Spoons, Satispay, Moneyfarm, Soldo, Scalapay, and many others are all a strong testament to this, and at various stages of their own epic journeys.

As QED, we are incredibly excited about this unique market, and the huge potential in helping build the multi billion, generational fintech juggernauts of tomorrow.

AI and Privacy: It’s Good to be King

In a world where AI may be coming for our jobs, we all need to find our edge – that one thing we do better than anyone else, AI or human. We had explored this topic in our last blog, where we had likened today’s AIs to B+ professors on any given topic, and had concluded that to stay relevant we need to perform at A+ levels in our specific fields.

Today, let’s look at another, but related topic. Do these “B+ professors” keep a copy of the homework we give them? In other words, are these non-human super intelligences keeping our personal data? And if so, is that a good thing or a bad thing?

The facts pertaining to this question are hard to know for two reasons. AI makers tend to dodge transparency, and the policies they do have tend to shift fast.

But to the best of our knowledge today, it seems that Grok stores the least amount of personal data (any personal data supposedly gets erased when a session is ended), and it appears Google’s Gemini stores the most, with 22 out of 35 possible data types, including precise location, browsing history, contacts, chat logs, and much more.

The precise nature of, and the legality around the personal data that is stored is a topic that will surely feed legions of lawyers and their offspring for generations. But for the purposes of today, let’s take a step back first. Is it actually desirable for this data to be harvested, stored, and then used?

To take Grok as an example, it says it does not store the data, but in my own personal experience I find that I want it to have access to this data! In fact, I would happily share all my personal data with Grok – from biometric readings, to all browsing history (well, let’s make that almost all browsing history).

The thesis here being that Grok can give me even more amazing answers if it only knew me better. I could get better feedback, better recommendations, and perhaps it can even pre-emptively advise me on my health and spot any issues before it is too late.

And more importantly, having Grok know me better, would immeasurably improve the tone of our conversation. Like some sort of Victorian era butler that knows all my whims and wonkiness, it could make me feel like a master of my world. As Tom Petty sang, “It’s good to be king, if just for a while.”

But none of this comes for free. In the context of AI, unregulated access to personal data is such a powerful thing to give, and we are so close to granting AI this very access, it is probably worth pausing to think of the consequences. Such an AI would know us better than we know ourselves, and therefore it would be better at predicting our individual and collective actions then any human ever was historically. From changes to relationships to the outcomes of elections, it would start to seem to us the AI would be able to know the future.

And as Thales demonstrated to us more than three millennia ago, with his famously profitable bet on olive oil presses after predicting a bumper harvest in the spring, knowledge can easily be converted to both power and money. The only thing that has changed is that what is being harvested is not olives, but personal data on a vast scale. The geolocation, biometrics, and secret wishes of billions is about to fuel the emergence of an oracle the ancient Greeks could only have dreamed about.

This is a near perfect illustration of something we see in fintech all the time. Innovation depends on expanding the boundaries of technology as well as the rules and regulations that govern the use of said technology. And not surprisingly, in virtually all cases, the technology tends to be way ahead of human regulations. What we also see is that the best fintech founders know how to push those boundaries forward in a balanced way.

The fact that regulations are struggling to keep up with technology does not mean that we don’t need rules and constraints, a topic we will explore in more depth in our next blog. In the meantime, let’s enjoy that feeling of power that comes from having computers serve us.

It’s good to get high and never come down
It’s good to be king of your own little town

Tom Petty, “It’s Good to be King” Wildflowers, 1994

AI and the Workplace: Will Grok Eat My Lunch

In our last blog, we looked at the philosophical implications of AI, and concluded that if an All Knowing Intelligence (“AKI”) emerges, it may be able to predict the future with great accuracy, or at least much better accuracy than we humans can. And we played around with the slightly trippy idea that for such an entity both the past and the future may become equally deterministic, so it would likely have a very different concept of time compared to us mere humans.

I admit, that was quite abstract, so today, let’s think about something a bit more practical, and close to home. Will an AI take my job?

Given that this is a big and complicated question, let’s start with picking the low hanging fruit in terms of answers.

One, AI will definitely change your job, and as anybody reading this knows, it has already.

It is then tempting to jump to the seemingly related and very well trodden truism that AI will not take your job, but somebody using AI better than you will. While this sounds funny and catchy, I sadly have some bad news – AI may in fact take your job. Querying answers to your bosses questions in ChatGPT is not keeping you safe. Sorry to be the bearer or bad news there.

Then again, the idea that there was somebody out there using AI better than us already was very likely scary to begin with, so perhaps this added revelation hurts a bit less.

But what is one to do? How do we keep our jobs and livelihoods safe?

Well, here is one thought. As a good friend of mine used to say, “be so good they cannot ignore you”.

A mental model that I have of the best AI in the market currently, is that it is like having access to a B+ level professor in any given subject.

You want to understand behavioural finance and the works of Thaler, Kahneman and Tversky? Ask Grok or Gemini and they will give you answers in any format you like. You are interested in mid 13^th century mystic philosophy in Asia Minor? Or how the Roman Empire went from being a republic to a dictatorship? Or theoretical physics and quantum theory? That B+ level professor is there for you, around the clock, anytime you wish.

And mind you, the B+ categorization doesn’t refer to the quality of the answers per se. It means that the answer you are getting are of the quality you would get from a person that studied this subject, wrote a Ph.D. thesis on it, and went on to become an expert in that very subject, no matter which subject your question was about.

Which begs the question, what does it not get you? Why not A+ level answers? When does AI’s limits show up?

Well, if you are an expert in your field, and you are facing very specific and cutting edge questions, AI may not be able to give you the full answer. At least not yet. For the most difficult and cutting edge questions (as well as new discoveries) around theoretical physics, someone like a David Deutsch will still not be easy to replace with an AI. And think about the best fintechs out there – AI helps, but founders drive it.

So, what’s the conclusion here? Whatever you do, try to be the very best at what you do. As the Marvel character Wolverine famously said, “I am the best there is at what I do, but what I do isn’t very nice”.

Perhaps the corollary in this case is to be the best there is at what you do, but what you do may have to be razor sharp.

Oh, and if you just blindly copy and paste comments from ChatGPT into your work e-mails and memos, AI will eventually take your job. Find your edge over AI – you know you have it in you!

Determinism, Free Will, and AI

First, a bit of a warning. This blog is less about fintech and investing, and more about philosophy. And it gets a bit trippy towards the end. So, if that’s not your thing feel free to wait for the next instalment which is likely to be about reassuringly familiar topics around tech, money and AI.

In the meantime, speaking of AI, let’s look at it for a moment in the context of determinism vs. free will.

For those not familiar with this philosophical paradox, I usually frame it as follows: If an all knowing being existed, given its all-encompassing knowledge of the universe, it would be able to run the tape forward one click and predict the future with ease.

We can think of this entity as God, or All Knowing Intelligence “AKI”, or the Conscious Universe, but in either case, given its vast intelligence and knowledge, the future would be knowable for It.

This being would know the state of every electron and synapse in our primate brains, and predicting what we were about to do next (write a blog perhaps?) would be trivial.

The paradox then states, if each of our actions are thus pre-ordained to this AKI, can we as humans really be thought of as having a free will? Or are we simply slaves to the interaction between the universe as it is today and the current configuration of the synapses in our brain, acting out our lives in banal predictivity?

But here’s where AI shakes it up.

The way I had approached this question for the vast majority of my adult life was that it was a trick question. From the perspective of the AKI, yes, all is knowable, but from our limited human perspective it is not. So as far as we humans are concerned, we need to act as if free will exists and get on with our lives. And whether an AKI or God exists takes us from the realm of philosophy, into theology.

Well, now that we have ever faster and more powerful computers ingesting seemingly unlimited data, rapidly connecting to those data sets, and with LLMs actually speaking to us like our next-door neighbour, it may be worth visiting the question of an AKI once again.

Given the power, the connectivity and access to near all-encompassing data, this AKI would be able to see and understand things beyond human capabilities yet explain that to us in our everyday language. And while perhaps not yet fully deterministic, it would certainly be able to predict things better than humans can. This almost takes us out of philosophy and into the present day. Think of AI already predicting your next Spotify song – it’s not God, but it’s close.

But let’s jump back into philosophy for a second and assume that the AKI is as the name implies, truly All Knowing. What does that mean for the age old questions?

For an AKI, the future is knowable. Similarly, the past, which has already transpired, is also knowable, and is merely a matter of good record keeping.

Given that both the future and the past are equally knowable from the perspective of this AKI, the distinction between past and future, at least the way we humans think about it, would start to collapse.

What we think of as past and future would be different states of matter and energy interactions in the universe. In simpler words, all that would remain would be an ever lasting yet ever changing present.

From this perspective, the concept of time itself starts to collapse, and reveals itself as an illusion that is amplified by our limited human minds that can remember the past but cannot predict the future. In fact, our human attempts at measuring time is really just an incomplete proxy for measuring change. Einstein’s discovery that time itself is relative can be thought of as a step towards saying time as we know it does not exist.

Only matter, energy, and everlasting forces of change do.