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.

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.

Your money on autopilot

Being very much a child of the eighties, I am fortunate enough to have witnessed many of our science fiction fantasies from that period become real. There are countless examples here, but one striking example is self driving, talking cars – and, yes, you guessed it – I am talking about Knight Rider here. While the Teslas of today do not leap into the air quite the way KITT used to, and talking to Alexa or Siri one does not get the same kind of relationship and life advice like that was generously doled out by the legendary black Pontiac Trans Am, let’s face it, we are pretty much almost there.

The concept of the self driving, intelligent machine understandably holds a deep fascination for many – and in the fintech community the corollary vision is very much being able to put your money on autopilot. There are different interpretations here, but we can imagine getting a scolding look (or perhaps deep vibration) from our handheld devices as we reach for that extra pair of shoes we know we don’t really need in the first place.

Not surprisingly, tech companies big and small are working on various aspects of this vision, and while there is certainly plenty of opportunity here to create the first truly intelligent money assistant in the consumer space (we have some strong contenders in the QED portfolio such as Albert), there is also an equally great, if not greater, need for this in the world of businesses and large corporations.

In your typical company, there is that trusted individual called the CFO, that is entrusted with the proverbial strings to the purse, and sits there overseeing the ebbs and flows of money coming in and leaving the company’s bank accounts. It is their responsibility that the bills get paid on time, that the employees of the company have the financial tools and resources needed to conduct their day to day activities of making and selling goods & services, and that the financial infrastructure of the company runs seamlessly.

A well functioning CFO organization can be the differentiator between success and failure for fast growing companies. This is even more important for innovative companies that are growing fast, and looking to scale across several geographies. These growing organizations need a finance function and back office support that is as innovative as the core product they are creating to continue fueling the rocket ship.

At QED, we believe that this vast set of activities that CFOs manage, with everything from expense management, KPI tracking, bill paying, and all those other financial back office activities is an area that is ripe for more and more automation over time. And observing our investments as well as the market over time, we have noticed that the sheer number of big companies created in the SME and corporate back office automation space is truly impressive.

Your money on autopilot: The dream is coming true after all these years.

To this end, we are very happy to announce our investment into Payhawk today – a company that we believe meet all three criteria for success in this space, and is positioned to grow into the preferred choice of CFOs across the globe as they look to put their back office on autopilot and supercharge their operations. These three criteria are having product and tech in their core DNA, being regulation agnostic, and being able to scale very fast across international borders. Some more thoughts on these characteristics is unpacked below.

Firstly, the best players in this space need to be very much product and tech led. In the end, it is a crowded marketplace, and there are lots of generic technologies out there that can be put to good use to automate many back office tasks. But as with any good tech product, the devil is always in the detail, and we find that the best companies are the ones that really obsess about the customer journey and the best technology to deliver it obsessively. Automation is simple – automation that offers a transformative experience for its users is hard.

Secondly, we find that in the business of back office automation, it is very important to be regulation agnostic if one wants to be able to scale like a true tech company. This means that it is preferable not to cross the dual boundaries of bank accounts and local accounting regulation. Banks are made to keep your money safe, and there is not reason to try to challenge them on that front. Similarly, local accounting rules can be complex, so it best to do the automation up to the pre-accounting level, and then leave the exact accounting treatment to local players that know that better. Now this does not mean that there is anything wrong with being a neobank or a tech driven accounting software, but if you want to scale rapidly, it best to stick to the back office automation only.

Thirdly, and as a direct result of the point above, we see that the best companies can also scale very rapidly across national borders. Again, this is a direct consequence of choosing to tread a path of not touching banking and local accounting, but in addition to that, it also requires affirming and choosing international scalability as a true north star. After all, strategy is very much about what one says no to, and affirming publicly and loudly what one stands for. A lot of product design choices then flow from this, and the end result is the company that fit this criteria end up becoming ideal choices for any company with international operations.

So in a nutshell, this is the story of our investment into leading Payhawk’s A round. And before Tesla figures out how to get their cars to jump (to be fair I recall seeing a photo of one is space) I promise to write an update to let you know how the journey is progressing.

Cascading Failures and the Importance of Diversity

A cascading failure is a process in a system of interconnected parts where the failure of one or a few parts can trigger the failure of other parts in a chain reaction that leads to the system shutting down or collapsing.

The first step in a cascading failure is typically an unexpected breakdown in one component of the system – this can be due to chance, human oversight, a black swan event, or any number of random reasons. Once this happens, other parts of the system must then compensate for the failed component. This in turn overloads these nodes, causing them to fail as well, prompting additional nodes to fail one after another. This is the textbook definition of a cascading failure.

While such failures can occur in nature, they are most often associated with manmade structures such as power transmission networks, computer networks, the world of international finance, and transportation systems to name a few well known and well publicized examples.

The reason that cascading failures are less common in naturally occurring systems is usually attributed to one overriding factor: Nature, in its literally infinite wisdom provides a seemingly redundant amount of richness and biodiversity embedded into its creations. In a normal evolutionary environment there’s enough diversity to cushion the system when something catastrophic happens. There is an abundance of compensatory pathways that can step in to rebalance the system.

Manmade systems, on the other hand, are different. In book two of the science fiction series The Expanse by James S. A. Corey, the scientist Praxideke Meng who is trying to create a farming ecosystem on Jupiter’s moon Ganymede says, “Nothing we can build has the depth of a natural ecosystem. It’s a simple complex system. Because it is simple, it is prone to cascades. Because it is complex, you can’t predict what’s going to fail. Or how.”

There are tons of implications of this, especially in today’s world where humans are willingly and knowingly destroying significant parts of the biodiversity that nature has generously given us over billions of years of evolution.

There are also very interesting parallels and insights for founders building an organization from scratch in the start-up world. Like building a farming ecosystem on a Jovian moon, creating a new company with a new product in new market is not easy. It is complex. But because of the limited resources the entrepreneur has available to them it also by definition has to be simple: You may want five different go to market strategies and dozens of sales partnerships, but in reality, you may be stuck with only a few. You may not want people to know it, but eighty percent of your revenue may be riding on one successful salesperson or one amazing sales partnership you managed to sign up.

Matt Damon made growing potatoes on Mars look easy in The Martian – starting a new business in a new industry can be just as hard

The key implication of all this for entrepreneurs is this: In a world where a diversity of sales channels and an abundance of resources are already scarce it is even more important to cherish and promote diversity wherever you may find it.

And the most important place to promote diversity is among the people of an organization and their way of thinking. After all, if the cascade starts happening, you will only have the people you have surrounded yourself with to help you stop it.

So at every chance possible, founders should try to promote a diverse culture with a richness of backgrounds and skills. When the time comes, it may be this richness and diversity that is the only thing standing between you and the cascade.

21st Century Lending

The earliest known records of lending date back about 4,000 years to Mesopotamia where the Sumerians were borrowing and lending livestock and seeds that would later be repaid from the offspring and yields harvested from the original “capital”.

Shortly afterwards the Sumerians discovered that it was more convenient to use silver as a medium of exchange, and not too long after that the Code of Hammurabi defined the price of silver and how the interest charged on silver loans was to be regulated. It seems that Hammurabi was quite concerned about usury and preventing abusive payday loan practices. If he were alive today, he may be disappointed (but perhaps not too surprised) to see that the need to regulate overly greedy lenders has not gone away. Four millenniums may have passed, but human nature does not seem to have changed much!

On the other hand, what has changed is the technology available to borrowers and lenders, and the good news is that there is now a new breed of lender emerging that uses this new technology to drive better outcomes for all parties involved.

This new type of lending is characterized by five main features: It is embedded, it operates in real-time, it unlocks and underwrites with sources of data that were previously locked up or inaccessible, the collection mechanism is “default on”, and the use of proceeds are laser targeted.

The net result of these five features is that the losses for this type of lending is orders of magnitude lower, in some cases approaching zero. Coupled with operational cost efficiencies enabled by technology, this in turn enables the lender to charge the borrower rates that are dramatically lower than other lenders, creating a virtuous cycle. Let’s now quickly examine all of these five factors in some more detail.

The first factor, embedded lending, has been written about and discussed a lot in the fintech community, including in my previous blog on the topic. In summary, it refers to the lending itself being invisible to the borrower and moving in harmony with the needs of the customer. An example we used in the past is how mortgages can be embedded into the property purchase to make the whole process last hours not weeks (or months as is the case in the UK). Imagine being able to buy a house with a few clicks!

Secondly, these new lenders operate in real time. In practice this means that they in real time ingest not just the data from the borrower, but also the third-party sources of data about the borrower, and hence can also make and communicate their decisions in real time. To take small business loans as a case study, consider how those were made in the past. The business owner would have to print pages and pages of financial statements that were already out of date at the time of submission, the loan officer would review these, and make the loan some weeks later. The business might have ended up in a totally different state by then, but again the loan officer would only find out once the new set of financial statements were prepared, reviewed by an accountant, and submitted some months later. Now contrast that with having real-time access to not just the customer’s bank account but also their sales data. Quite a difference!

One can only hope for lower interest rates in the future!

Thirdly, yes, the data is in real time, but crucially it also contains new and relevant information that is usually locked up inside the business. This can include anything from recent bank account movements to real time sales data or data on how many shifts an employee has worked that week. This incremental data not only provides better performance from a credit underwriting perspective, but it also ensures way better customer outcomes by making sure calculations like affordability and the potential vulnerability of the borrower can be made with superior precision.

The fourth factor is a collection mechanism that is “default on”. In simple terms this means that in the ordinary course of business, the collection of the loan is automatic and does not require any extra effort. Following the insights of behavioral finance that have emerged over the last decade thanks to Professor Richard Thaler from the University of Chicago (where I had the good fortune to take his class) and his mentors Kahneman and Tversky, this actually makes the loans much easier and painless to collect, resulting in substantial costs savings that can in turn be passed onto the borrower. A good example of such a mechanism is collecting a portion of sales receipts at the point of sale to repay the loan, but there are many other examples.

Finally, the use of proceeds is targeted with laser precision. The loan is not made as a general disbursement for the borrower to spend as they wish, but rather for a specific purpose. Another way to look at this is that the lender understands very well why the borrower needs the funds, and how that fits into a sustainable pattern where the borrower can pay back the loan without falling into distress. This leads to a much more borrower friendly situation, where funds are only drawn down subject to affordability and a sensible use of proceeds.

In closing, it is also very important that one does not lose sight of the most crucial element of the narrative: Done rightly, 21^st century lending enables far superior borrower outcomes where consumers and small businesses can borrow money when it is most needed, at a fraction of the cost, with minimal disruption to their lives, knowing that affordability and sustainability has been embedded into the process.

Building the Unbuildable

An interesting concept in architecture is the idea of “unbuildable buildings”. These are designs that when once finished would be able to stand on their own as sustainable structures yet cannot be built as the intermediate steps needed to create them are not technically feasible.

A good example of this may be a building that rests on concrete pillars in a wavy sea. Once built this may be a beautiful and stable structure, but if the intermediate step of pouring concrete into the sea and letting it solidify is not technically possible it cannot be built. Only as technology improves and develops sufficiently, pouring concrete into the sea may become possible, at which point the building can then be built.

Another example could be a colony on Mars – while a fully functioning dome with solar panels and atmosphere control could be a sustainable and self-standing structure, the intermediate steps of setting up a construction site on Mars and shipping workers and materials there is not economically and technically feasible, at least not as of yet.

The same concept actually exists in the world of evolution as well. There are theoretical organisms that could possibly exist in our world, yet do not because the intermediate steps needed in their evolution were not possible. Put another way, any species that exist today (e.g. birds) do so because each step in their evolutionary journey (e.g. developing wings) was sustainable. Prior to wings, the ancestors of today’s birds developed wing-like appendages that enabled these bipedal species to leap higher into the air, and thanks to whatever small advantage that gave them, they were sustainable as an intermediate step to developing the full-fledged wings of today. If the intermediate steps had not been sustainable, wings would not exist.

There is clearly a very interesting parallel here for the startup world. A founder’s end vision for their company may very much be possible, but if the intermediate steps needed to build the company from zero to one are not possible, the founder may never reach their end vision. These intermediate steps may not be possible for a whole host of reasons and include the current state of technology, availability of financial infrastructure, the funding environment, and the applicable legal and moral rules, to name just a few.

For example, if the intermediate step of creating the end vision requires a multiyear journey with heavy losses and lots of big investment along the way, that particular company may not be buildable in a bad funding environment, but could in fact be built in a situation where there are deep pocketed investors willing to fund these losses for many years. Similarly, if the technology or regulatory license needed is impossible to obtain for a small company, until the regulatory environment or technology improves, the company cannot be built.

The big takeaway here is that for a founder to have an end vision that is self-standing and sustainable is not enough. The intermediate steps needed along the way also need to be self-standing in their own right, and the founder has to pay very close attention to what those steps are.

By extension, successful founders are those with a very keen eye for changes in these constraints. Things like infrastructure, regulations, and funding environment are very much dynamic and in constant flux. As laws change, new technologies become available, or central banks decide to pump trillions of dollars into an economy, previous constraints may suddenly be lifted. The best entrepreneurs are the ones ready to pounce quickly when they sense these changes that make their previously unbuildable visions buildable.

To illustrate this point, an interesting example worth taking a deeper look at is the building of a new exchange for residential real estate investment properties. Known as buy-to-let investing in the UK, these are the kinds of properties where landlords buy them to rent them out for a profit.

Currently this is a fairly illiquid market, and rather than being traded on an exchange, it is traded in one to one transactions where buyers and sellers find each other via property listing sites such as Zoopla (which are a big improvement in their own right as in the past one would have had to go to the listings sections in newspapers). Once buyers and sellers find each other (after lots of viewings) and agree on a price (after tense negotiations), it may still take weeks, if not months before the transaction actually closes. When all is said and done, the process will have involved real estate brokers, lawyers, viewings, tons of paperwork, and lots and lots of costs for both sides.

Contrast this with somebody that has invested in a company via the stock market – they can buy and sell their shares instantly because the ownership certificates (shares) are standardized and their trading is regulated by the exchange (e.g. London Stock Exchange). We can think of these standardized certificates as “securities” and the process of taking a non-standardized, clunky asset and turning it into an easily tradable security as “securitization”.

But why should one not be able to “securitize” buy-to-let investing? The end vision is surely feasible with today’s technology: Each property could be owned within a legal structure (a company) with a standardized legal contract. Then the shares in those companies could be traded on a big exchange that lists thousands of those companies with standardized and regulated ownership structures, and one could browse and filter properties, make an offer online, and trade it with a few clicks. While this process may still not be instantaneous like buying stocks today, the time to transact may nonetheless be cut down from the months and weeks that it takes today to days or perhaps even hours.

One obvious difficulty here is the intermediate step of bringing together the supply and demand (the buyers and sellers of property) under one roof on day one. If there is not enough supply then the demand will not show up, and vice versa. Hence, the entrepreneur that wants to build this new exchange will have to find a way to solve this conundrum by bringing the sellers and buyers onto the platform in incremental yet sustainable steps. If this can be cracked, the pot at the end of the rainbow surely is full of gold: buy-to-let properties represent an asset class of GBP 1.2 trillion in the UK alone and are currently housed in very illiquid structures. Solve the intermediate step, and the end vision is yours for the taking!

To summarize, having the courage and stamina needed to be an entrepreneur is one thing, but the best founders also need to be the kind of visionaries that see changes as they are about to happen, understand how those changes enable cracking the difficult intermediate steps, and imagine what kinds of future structures all this enables. With that kind of vision, what was once impossible and in the realm of science fiction, becomes reality.