The Trading Show is an esoteric conference that brings together people who work in niche areas of fintech like algorithmic trading and increasingly in digital assets and cryptocurrencies. I was invited to speak at the Chicago event last month on the subject of Fintech and its evolution, which presented a good opportunity to introduce our investment thesis for the next series of funds from Applied Crypto Ventures.
From Blockchain to Fintech 4.0
Back in late 2017, when Applied Crypto Ventures first fund announced our framework for investing in Blockchain, we were very focused on blockchain protocols at two levels:
- Cryptographic innovation to establish new secure distributed ledgers (also called Layer 1 protocols) like Bitcoin, Ethereum, Hashgraph, Algorand, etc.
- Adding scale to existing blockchains (also called Layer 2 protocols)
We believe that although projects will continue to innovate at both layers, the excitement has shifted to early applications and especially to Fintech and decentralized finance. As many as 11 of our 27 positions in Fund I are focused on Fintech, perhaps because financial services are usually early adopters of cutting edge technology and because of the team’s roots as VC investors are in the fintech sector (Fidelity Ventures, F-Prime Capital, Eightroads, Liberty Mutual).
Looking ahead, we believe that the next few years will see a rewriting of the fintech playbook based on the convergence of a discrete set of technologies with advanced applied mathematics at their core and feeding on the data exhaust of today and the future: AI, Blockchain, IOT and Quantum computing. This prophetic view is increasingly referred to as Fintech 4.0 and this is where ACV Fintech Next Fund II will be focused on investing.
Read on to explore why.
Technological revolution occurs in distinct waves
The fourth industrial revolution is upon us — the use of AI, Robotics, Cognitive computing — will improve upon what previous technological waves did for humankind:
- Steam allowed us to eliminate human drudgery in production
- Electricity allowed us to use lighting and assembly lines to make production more efficient
- Computers allowed us to make manufacturing faster and creating digital products and services that can be time-shifted and place-shifted
There is a similar evolutionary track in Fintech over 3 waves.
- Paper processes flourished for nearly 200 years since traders stood under in a tree on Wall Street
- Digital Transformation of the 70s to the 00s allowed us conduct banking and other financial services faster and over distances
- Startups focused on niche markets have been able to pick off big chunks of the market served by large behemoths by providing better/faster/cheaper customer experiences
The future of fintech is rooted in Data
The evolution of the internet, Web 2.0 services like social media and chat, the mobile computing revolution, and cloud computing have all generated a massive amount of data. The advent of “Big Data” in the 2010s and and the rise of analytics and data science delivered results for presidential candidates and for dads picking up beer and diapers on their way home.
And we believe that distinction is where technology and especially fintech will thrive: data needs to be secured, processed, and mined for information and intelligence that will then drive the adoption of new ways to monetize this data. Financial services will be the some of the earliest and likely the largest beneficiaries of these developments.
Of the many computational paradigms that will impact Fintech greatly, I believe Artificial Intelligence(“AI”), Blockchain, the Internet-of-Things (“IOT”), and Quantum Computing (“QC”) are individually going to drive waves of innovation and then converge in combinations that will deliver the next generation of financial transactions: intelligent, secure, decentralized (potentially trust-less), macro and micro transactions, at scale.
In my view, AI models, blockchain protocols, IOT networks, QC architectures are all going to end up being baseline infrastructure. It is the data flowing through them that makes them value generators — and thereby data is like oil, in that it needs the midstream and downstream infrastructure to realize its potential in an internal combustion engine or as the seamless contactless checkout experience at an Amazon Go store.
AI needs a supporting cast
AI has been around for a few decades now but only recently has it been getting better — McKinsey says AI is poised to disrupt our world. Within the broad scope of AI, machine learning (and deep learning in particular) has revolutionized computing both visibly (as in face detection in social media or at airports) and invisibly (flagging fraudulent credit card transactions or improving Google search results via RankBrain).
But AI algorithms and infrastructure are rapidly getting commoditized and AI is constrained by the amount and quality of the data
— Forbes Technology Council (read more)
For example most object recognition systems built into cameras widely used today are based on a handful of pre-trained models.
Business advantage is now derived from leveraging “machine learning” and “deep learning” trained on proprietary data sets. As we get into a world where AI based revenue models increasingly seek proprietary data, the provenance of data and compensation provided to data providers will become increasingly more important.
Three technologies will become mission critical in this context: Blockchain, the Internet of Things (“IOT”), and Quantum Computing
Blockchain will provide trusted transactions
A secure immutable system of record can establish ownership of data assets and its provenance. And cheap payment rails made possible by blockchain (not necessarily tokens or cryptocurrencies, as demonstrated by both Ripple and JPMCoin) will make reliable and ad-hoc data sharing possible.
Consider a hypothetical use case: the sharing of clinical data by several provider organizations (like research hospitals) which can help refine AI based drug discovery or diagnostics. This is not so far out since providers are already doing this to improve quality of care. Data providers will expect to secure and protect their data assets, retain title, and expect reliable compensation for present and future revenue opportunities based on data science that leveraged the data.
In most industrial ecosystems, and especially where fintech will facilitate exchange of value, Blockchain will deliver:
- Provenance: blockchain can securely establish which data belongs to whom and who can access it and when
- Trust: smart-contracts can enforce rules of sharing data,
- Payments: ensure that data providers get paid for downstream royalties or services supported by models generated using their data
- Cheaper transactions: in their decentralized/trustless form, blockchains will eliminate counter-party risk without middlemen and hence lower the overall cost of transactions
IOT makes data available and usable in real time
Data interchange is increasingly between machines and the real time nature of these exchanges can be invaluable to acceptable operations of AI based systems and other data-driven decisions.
Some potential use cases: imagine a road monitored by a video camera intended to provide real time intelligence to a self driving car or a robot. The ability of the camera to detect events locally and communicate them to a machine navigating through its vicinity, requires systems to both digest the information reliably and also compensate the providers of such signals. One popular example cited by Naval Ravikant is the real-time negotiation between two self-driving cars who arrive at an intersection simultaneously. One of them can pay the other to pass first. The NHTSA is already discussing vehicle-to-vehicle communication so this use case is not so far-fetched.
AI and Blockchain can provide the cloud-based capacity to deliver on this value but IOT will go the last-few-feet to retrieve data, and confirm payment for transactions.
Quantum Computing will help us keep up with the data deluge
We have been generating 463 Exabytes (1 EB = 1 Billion GB) every day this year, and this data problem is just compounding. Moore’s Law that predicted the increase in our computational capabilities was physically maxed out in the last decade. We need a new mechanism to be able to deal with this ever increasing amount of data so that we can realistically analyze it and learn from it in near real time. We are fast approaching a time when it will be practical to harness it so we can continue to make sense of our own data exhaust for applications like fintech.
Fintech 4.0 is blockchain Investing in a post-ICO era
Although cryptocurrencies and tokenization will remain interesting as investment opportunities, the regulatory environment in key markets like the US, China, and India casts a shadow over the purist view that blockchain networks will supplant traditional finance in the near to medium term.
Current trends in business model innovation centered on blockchain are veering towards tokenization of existing assets (e.g. art, real estate, equity) and other cash flows. While this may be interesting to some entrepreneurs and investors for purposes of creating more liquid markets, we do not consider this to be our charter as VCs.
We seek technology innovation for unlocking investment opportunities.
Blockchain tech innovation is now moving into the application domain leading with fintech, and as experienced investors with a successful track record in Fintech 3.0 (Xoom, Alibaba, Prosper, Flywire, MineralTree) and early inroads into Fintech 4.0 (Circle, Algorand, Kensho, FutureAdvisor, 0x, TrustToken), we see this as the logical evolution of our investment focus.
If you are curious about Fund II (now open) or how Fund I is doing (very well!), sign up to learn more.