Doing my analyst program at Accenture Ventures and writing my Master's thesis on the influence of CVCs on a startup success rate has bolstered my curiosity in this unknown-but-critical function of a corporate. The recent emergence of CVC units, actually 100+ CVC programs launched in 2022, motivates me to finish this quick article to share some thoughts on Corporate Venture Capital.

I'll review what's a CVC, the forms that a CVC can take, the pros/cons of CVCs, and last but not least, the outcomes of my master's thesis/my experience working for a strategic CVC.

What’s a CVC and its function?

We are currently in the golden age of the new Consumerism: better products and services, but also cheaper with an extreme velocity of adaptation to answer the boundless demand (i.e. Shein). The product GTMs are getting shorter and shorter, with more complex features, and the cost of market entrance is lowering, which leads to the competition being fiercest than ever.

Enterprises understand that Technology, per extension Innovation, is currently becoming a critical layer that must be constantly considered to survive in this era. It represents a new variable able to short, disrupt, accelerate, or shift the current standard or consumption behavior. Technology becomes a major component of a company's success – no matter the industry or the size.

[Kick in the western saloon doors] There enter CVCs. Hold on. But first: what defines a CVC?

According to GCV, it could be defined as " Corporate venturing involves larger companies investing in and supporting entrepreneurs, such as taking minority equity stakes, either directly or through venture capital funds, as well as other innovation tools, including incubators, accelerators and developing internal innovation – ‘intrapreneurship’.” I think it perfectly summarizes a CVC's general activities: the corporation's innovation scouting tools.

From my perspective, I believe the cost of entrepreneurship & access to Innovation has never been so cheap in Human history, which pushes forward the importance of CVC units to support R&D projects, Product Development, Corporate Dev/M&A deals, or BD initiatives to gain agility and stay competitive. A CVC can meaningfully reorient a corporate in the medium- to long-term with zero operational impact on today's core businesses.

As a huge fan of MOBA games (mainly League of Legends), I like to use the analogy of the jungler role to illustrate what is a CVC: it's an agile unit that is dedicated to supporting, defensively or offensively (i.e. ganking), other divisions [lanes], interfacing with external parties, and providing strategic insights (i.e. warding) to your teammates. The jungler [CVC unit] gives a considerable advantage in map awareness [ecosystem knowledge] and presence [access to technology] over a team without a jungler. One word – Swiss knife.

I think the core function of a CVC can be split into two parts:

The offensive mode:

• Help the parent company stay competitive and enter new markets by embedding the external innovation, provided by startups, into the firm's activities and strategy.

• Gain a “window view” on emerging technologies/products aligned with the company’s activity. (i.e. Microsoft investing in Open AI)

The defensive mode:

• Acquire new technologies (especially patents) developed by emerging startups to strengthen their expertise/presence in a given market.

• Block the parent company’s competitors to access one specific market/technology/industry or prevent forthcoming challengers to gain more market shares. (i.e. Adobe acquiring Figma)

Large corporates can be represented as an elephant: this animal is clumsy and slow, and the CVC program helps to be more nimble, fast, and ubiquitous. CVC helps parent firms to develop or strengthen their products, market presence, or expertise by leveraging exogenous elements, such as venture investments or strategic partnerships.

Despite the recentness of the CVC function in the corporate landscape, it plays a critical role in large companies’ technology development, competitiveness, and strategy. I strongly believe we will see more and more CVC programs in the future as Technology & Innovation become mandatory to any firm’s survival/growth and push back the quarterly reports pressure.

The different forms of a CVC unit.

There are as many shapes of CVC programs as startups in the world. Below you will find the major different forms of a CVC.

A few comments:

• The more the CVC looks like a traditional VC, the more it’s likely to invest.

• The major differences between a CVC and a “strategic” CVC are: one invests at the “industry level” while the other is at the “business level” of the parent company. For example, Intel Capital has massively invested in the SaaS segment despite the parent company’s core business being the design and production of chipsets. Secondly, strategic CVCs will likely have a “partner then invest” approach – which tends to slow the investment process as startups have to go through time-consuming processes.

• Some corporate can have multiple CVC programs, for example, Google with CapitalG, GV, Gradient Ventures, and Google for Startup Accelerator.

The pros & cons of CVCs (from the startup & VC perspective).

Despite being in the market for a while, CVCs had this black sheep or “buy high and sell low” reputation in the venture ecosystem given the instability over time ( i.e. programs shut down/ variable annual budget allocations, unrealistic operational maturity timelines, staff turnover…), being slow/hesitant in deal executions, or the numerous potential conflicts of interest (i.e. balancing the corporate long-term interest with the startup short-term interest, unaligned incentives), to name a few. However, things have changed recently, I believe COVID-19 has played a substantial role in the Digital Acceleration/Technology Adoption amongst corporates. CVCs are getting more space in cap tables and becoming more venture-experienced, CVCs have participated in 26.2% of venture deals in 2022 and can efficiently leverage their technical/industry expertise, from all across the firm, to be value-added for the venture ecosystem or the portfolio companies. You probably know that each category/type of investor comes with its ups and downs, CVCs don’t make any exceptions.

From my PoV the pros & cons of a CVC (from a startup perspective) would be:

(+) Get a strong technical/market/brand validation. Having a headline with “[a startup name] secured a $X million round from [ a large corporate name] blasted across all the social networks and breaking news de-risk and validates the company.

(+) Access to market/technical expertise to reduce the time-to-market and accelerate market validation. Having access to a pool of subject matter experts and supportive executives that understand where your pain points are, how to solve them, what is the market trend, or which key people within the industry you need to be introduced to can drastically impact the success/survival rate of the startup. During my time at Accenture Ventures, The overall strategy was/is to provide the early-growth startups with Accenture's technical expertise combined with the clients' market expertise to reduce the market validation and time to scale.

(-) The double-edged sword – Partnering with a CVC that understands the role of a CVC can bring the startup some significant advantages that go over the financial/business support. However, a CVC requires so many external factor alignments to be performing (strong support from the parent company’s leadership, profiles that can navigate between the corporate and startup world, an extensive network within the organization, and the adequate budget to follow up the actions) and value-added for the startup. If all these requirements aren’t filled, the CVC will likely be a burden than an asset. I personally believe that a CVC is a double-edged sword game: all-in or nothing.

(-) The CVC parent company stamp – Having a big logo name associated with your name is not always a door opener. some competitors of the investor's parent company, stakeholders in the value chains, or even the whole ecosystem, will be reluctant to do business given the fact that the startup can potentially share sensitive information. One great example that perfectly illustrates the situation: an IoT-focused startup in my network raised its Series A round including the leading Chinese TMT firm – good to mention: partially owned by the government – the founder has difficulty closing deals with EU customers and securing new funding.

From my PoV the pros & cons of a CVC (from a VC perspective) would be :

(+) Get a deeper review of the startup’s technology feasibility/product viability. I believe CVCs often have an edge when it comes to assessing the technical relevance of a given startup's product/technology more precisely than traditional VCs do. CVC investments provide a great overall signal of the technology/product readiness, a good proxy of the market needs, and a rough measure of the risks associated with the investment. (except for the advanced/big tech corporates that pour billions into unknown technologies like Quantum or Fusion, the majority of CVCs have a risk aversion higher than traditional VCs). Financial VCs can find CVCs a powerful ally.

(+) Financially stable investor over time. Most of the CVCs are structured to invest from the balance sheet or an evergreen fund, and some of them are deploying from a single LP fund. At the opposite of traditional VCs, the majority of CVCs don’t need to compete for LP allocation and worry about fundraising, saving cash for down rounds/bridges given the market conditions, or being extra-sensitive to startup valuations. CVCs are likely to double down in the subsequent rounds. The single exception is the CVC program shutdown, or the budget allocation canceled.

(-) Slow in deal execution. When some financial VCs can close a deal in two weeks, CVCs will require significantly more time to execute the investment. CVCs (except financial CVCs) often have to comply with the parent company’s processes (i.e. the IC only once a month/quarter), security standards (i.e. Personal due diligence of the target company), and regulations (i.e. IFRS/US GAAP accounting conformity), which considerably delays the investment process.

The outputs of my master’s thesis and my personal CVC experience.

Last but not least, the outcomes of my master’s research thesis and my experience in the CVC side.

Through my research thesis and my time as an analyst for a strategic CVC, I got the chance to interview/speak with +50 CVCs, CVC-backed startups, or investors co-investing alongside CVCs. Being exposed to many types of CVCs enhanced my appreciation and understanding of this particular form of venture financing. It has helped me to find an answer to the question " CVCs are really helpful to startups ?” and “What makes a good CVC?” – On paper, CVCs look like the ideal investor: extensive market knowledge, strong technical expertise, a solid network, and a deep pocket to fund startups through their journey. But the reality is something else, according to GCV research, +2/3 of corporate-backed startups fail, 50% fail within 3 years, and the majority of CVCs fail to make it beyond three years.

From my research, I found that :

• CVCs with a parent company deeply involving technology/technical expertise in their core business tend to be more impactful on a startup’s success as technology-driven corporates give more than they get to the startups.

• CVCs investing from a separate entity perform more than those investing from the Balance Sheet. As it has its own investment committee/process: it speeds up the deal execution, reduces unnecessary red tape, and avoids potential conflict of interest. Internal/Balance sheet-based investing CVCs tend to be slower.

• The best team composition for CVC teams’ structure is ½ from the parent company and the traditional VC industry. The greatest value addition of CVC is the combination of internal and external knowledge: the business/technology expertise of the parent company and the innovation/deal expertise of traditional VCs.

The major challenges that CVCs face are:

• Have stability over time. (CVC programs can significantly be influenced by internal politics, management change, budget allocation, and so on…). The success of a CVC relies on its capacity to perfectly align internal stakeholders with external factors at a given moment.

• Stick to the mission and measure the strategic return for the parent company if applicable. (Capturing the financial return is more straightforward than the strategic return metrics. And CVCs can easily drift from the initial missions, there is a fine line between a financial CVC, a CVC, and a strategic CVC). As an example, Intel Capital laid off ¼ of the Investment Team in 2018 to make a smaller number of larger deals more closely related to Intel’s core business.

• Retain employees and avoid conflict of interest between the CVC management and the parent company executive. (The fact that most CVCs don’t provide carried interest to the management team plays a crucial role in retaining talent). The Xerox Technology Ventures (XTV) case perfectly pictures this situation: “The structure of the program provided executives with hefty compensations and led to turmoil between Xerox managers and the XTV executives. It was also believed that the startups that were part of the program might have succeeded at the expense of other Xerox units.”

I hope you enjoyed reading this blog post! if you have any questions and comments, or just want to chat, please feel free to reach out to me.

TL;DR

Data Compression – Existing compression algorithms are not adapted to 21st-century technologies and business needs. The global data generation rate and the surge of new data-intensive applications make more critical the need for new data compression methods. It brings new challenges related to Bandwidth Consumption, Energy Usage, Data Transmission, and Data Storage.

Synthetic Data – The emergence of AI/ML technologies will participate in the acceleration of Synthetic Data. It is cheaper and generated quicker than real data. By 2030, most of the AI models will be trained with synthetic data. And it can contain no identifiable information and helps in addressing privacy concerns (GDPR, CPA…) and biases related to sensitive real data.

AMaaS – I didn’t expect this trend will be so big. It was simpler: just give access, democratize, and educate people about Asset Management and Personal Finance. It concerns all types of asset classes: Angel investing/Venture investing, digital assets, real estate investing, and more. Personal Finance/Asset Management is getting more traction across the GenZ/Millennials segment, not only as a hobby but also as a must-do for closing the generational wealth gap.

Data Compression

We are at the beginning of the Data Era. Data has become the bottom layer of our modern society. Companies are making use of data instead of making data useful. Everything, no matter if it’s online or offline, can be collected, exchanged, processed, and enriched to be able to deliver value. However, data size, type, and quality have constantly evolved over the decades. Two problems arise: firstly, data creates more data, and secondly, existing compression algorithms are not adapted to 21st-century technologies (IoT, HPC, Satellites, AI/ML…), and business needs (Real-Time Processing, restricted/limited devices, media contents…).

A quick example that could speak to everyone about why data compression is critical now more than ever. Old iPhone videos (5G/6G) sizes approx. 60MB/min and the most recent iPhones (12G/13G) require at least 300Mo/min. Based on the 1st iCloud plan, now, each video's minute stored costs $0.006/Mo/month with max. capacity of 166 minutes. With the old generations, it would only cost $0.0012/Mo/month with max. capacity of 833 minutes. It perfectly illustrates that technical specs have evolved over the years, but data/file compression technology hasn’t. The cost for users has increased by 5x while the effective storage capacity has been reduced by 80%.

The massive data generation brings up new challenges related to Bandwidth Consumption, Energy Usage (you’ll be deadly surprised as me when you’ll learn about the energy consumption of data centers…), Data Transmission, and Data Storage. I’m confident that Data Compression will be a top trend for 2023:

• The surge of new data-intensive applications like Edge Computing, Analytics, AI/ML, or IoT has heavily contributed to Data Generation. It makes more critical the need for new data compression methods. Data create more data. by 2025, +175 zettabytes of data will be globally generated, with half of it solely produced by the 41.6 billion IoTs, and 80% of data worldwide will reside in enterprises. The global amount of data is exponentially increasing in parallel with the adoption/development of intensive data-generative technologies, which at the end of the day has a tremendous impact on the bottom line (Yes, I agree – cloud storage is getting cheap but what's the point if data is exponentially compounding.) and the IT infrastructure. One great case study gives a good hint about the importance of Data Compression for today’s businesses, a 750-truck fleet company has reduced its telematic data volume per vehicle by 64,9% and divided its yearly satellite cost by 3x thanks to AtomBeam’s technology. The company’s annual saving represents +$1M with this Data Compaction solution while the annual software cost is only $50K with a payback period of 3 weeks. Now, let’s scale it up for one second. What would be the (financial & environmental) impact on the whole Tesla fleet?

• The technology industry has a long history of buying out data compaction/compression startups (e.g. Twitter acquiring Magic Pony or Apple purchasing AlgoTrim). Data Compression technology has already attracted governments and major tech firms (i.e. Facebook with Zstandard). The colossal Data space is projected to reach $655.53B in 2029 at a CAGR of 13.4%. It doesn’t even account for the market expansion of intensive data-generation technologies (IoT, AI/ML) and data consumption-based services (VoD…). I’m confident the Data Compression segment represents at least a +$1B market opportunity and can address technical issues for multiple industries.

As the amount of data generated/consumed continues to grow exponentially and data-intensive technologies become widespread, the need for new compression methods has never been greater. The subcategories of the Data Compression space to watch are Lossless Data Compression, ML-enabled lossy Data Compression, and Data Compaction.

Synthetic Data

Thanks to Jasper’s $125M Series A round valued at $1.5B and ChatGPT, Generative AI is on the front row of the global tech scene. You’ve probably come across the fabulous article of Sequoia on this topic. Generative AI has so many applications, from content creation to 3D environment generation. It can literally change our approach to work. I’m particularly interested in the AI generation subdomain: Synthetic Data. It sits at the intersection of Generative AI and Privacy Enhancing Technologies (PETs) spaces.

Synthetic Data is artificial data generated by a computer program or algorithm without any reference to existing data. It is often used as a substitute for real-world data in ML and other data-intensive applications, as it can be generated quickly and inexpensively, compared to real ones, and it can be customized to fit the specific needs of a particular project or application. It can benefit multiple industries (Financial Services, Healthcare, Industry 4.0, Defense & Security…) and several business functions (Data Scientists, Software Engineers, HR, Marketing…).

The underlying requirement of well-functioning AI/ML technology is qualitative data, a really large amount. It's like car mechanics, if you fill your tank with ethanol instead of petrol [Data], your car will still run, but there is a high chance you won't get the expected performances or the engine [AI/ML] will degrade over time. In this analogy, synthetic data would be “generated” petrol, like biofuel, with properties similar to "organic" petrol. And like in the real world, good data can be as expensive and rare as petrol. I strongly believe the emergence of synthetic data will happen in 2023 :

• Companies often struggle to collect large amounts of data and train accurate models within a specific timeframe. The issue is that collecting and labeling datasets, which could contain thousands to millions of data, is time-consuming and often exorbitantly expensive for AI/ML teams. A single image could cost $6 from a labeling service while it can be artificially generated for $0.06. Additionally, some data don't even exist due to the rarity of the real-world event (what's the probability of finding a video of a kid crossing a road in snowy weather conditions to train your autonomous cars ?). Gartner predicts by 2030 most of the data used in AI will be artificially generated. Synthetic Data significantly help improve ML models' accuracy and reliability in a cheaper and faster way.

• It's not a secret anymore that data is the new oil. People are fighting for and debating about data. Recent regulations (HIPAA, GDPR, CCPA, CPA) have forced companies dealing with users' data to comply with some stricter requirements, and the multiple data breach scandals place favorable conditions for the Synthetic Data & Data Privacy spaces' growth. Synthetic Data contains no identifiable information and helps in addressing privacy concerns and biases related to sensitive real-world data. Data can be shared or processed by a third party without privacy issues. It can be a game changer – mostly for the healthcare & financial services industries – American Express beefed up its AI-based fraud detection model training with synthetic data as this type of data is sensitive and this type of event rarely occurs.

Asset Management as a Service (AMaaS)

Writing about this trend earlier this year motivated me to keep an eye on this emerging market. Spoiler alert: I was totally wrong. My focus was leveraging a technical stack (i.e. Blockchain or API) to give access to Asset Management services. It was simpler than that: just give access, democratize, and educate people about Asset Management and Personal Finance. I didn’t expect this trend will be so big. It concerns all types of asset classes: Angel Investing/Venture Investing, digital assets, real estate investing, and more.

• I almost fell off my chair after coming across the crazy metrics of Yoann Lopez’s newsletter about investment & personal finance: Snowball. The newsletter-turned-super app for Personal Finance/Investment attracted +23.5K subscribers (Feb. 2022), among which +4K are paying. He managed to reach €225K ARR in two years with 0 marketing budget. These numbers are insane – approx. 17% of its users are paying/ready to pay for tips on Personal Finance, which is free in its classic newsletter and abundant over the web, and an upcoming super app for Investment. As the data only concerns the French market, I strongly guess that more people would be willing to pay for a similar product on a global scale. Yoann's project genuinely illustrates the rising AMaaS trend: the 20-40 understand that Investing is mandatory to build a future, they are willing to pay to learn more about Personal Finance or access personalized Asset Management services on a subscription basis, and anyone can monetize in-depth industry/market knowledge to become an Asset Manager.

• GenZ/Millennials are getting more and more exposed to Finance topics across all channels. FinMeme accounts largely participate in the democratization of the Finance & Investment ecosystem. The top performers gather +1M subscribers across social networks, and some have even gone beyond the social/digital sphere to reach the real Finance industry. For example, Litquidity has signed on as a venture scout for Bain Capital Ventures. People slowly start to develop knowledge in this space, fully understand that investing is mandatory for their future, and want to learn how to construct a portfolio. Personal Finance/Asset Management is getting more traction across the GenZ/Millennials segment, not only as a hobby but also as a must-do for closing the generational wealth gap.

I hope you enjoyed reading this blog post! if you have any questions and comments, or just want to chat, please feel free to reach out to me.

The surge of new data-intensive applications like Edge Computing, Analytics, AI/ML, or IoT has contributed to the exponential growth of data. A great example from Data Center Magazine highlights the issue “By 2025, connected devices alone will generate an estimated 79 zettabytes of information. […] In 2016, the entire volume of all data on earth amounted to just 18 zettabytes - or about 720bn Blu Ray copies of Blade Runner: The Final Cut”. 80% of data worldwide will reside in enterprises in 2025.

There is an interesting chart from the Data Gravity Index report explaining the relationship between Data Creation/Generation and Data Gravity. In simple words: you can create data from anything, you can aggregate enriched data to get new insights, and repeat the process over and over to get more valuable insights. As the dataset grows, it becomes difficult to move it.

Hence, Data Gravity matters more than ever. The term is quite interesting; let's shed some light on it.

What is Data Gravity?

The term owes its origin thanks to Dave McCrory. In 2010, he coined the term "Data Gravity" on his blog to explain the attraction of applications and services for massive datasets. McCrory drew an analogy based upon the laws of Gravity, which viewed a considerably large dataset as a massive body or a planet in the universe of data. As the increase in the mass of a body leads to greater gravitational pull towards the massive body, snowballing of a dataset causes the applications and services, which are smaller datasets, to be pulled towards the massive dataset. When the data accumulate enough, it is nearly impossible to move, so the services and applications are forced to converge toward the data location to maintain a certain level of performance.

The proximity to the data affords the additional applications and services greater Throughput and lesser Latency leading to more excellent reliability of the applications and services. However, moving closer to bigger datasets would force to on-premise or full-on one cloud vendor. Below there is an illustration of the concept.

And here is an interesting formula provided by John McCrory to calculate Data Gravity by taking into account all the important variables involved: Latency, Bandwith, and Data Mass.

The surge of Data Gravity

Ten years earlier, McCrory was able to anticipate the phenomenon of Data Gravity. Today the numbers tend to corroborate what he envisioned. According to a report of the Data Gravity Index, G2000 Enterprises will produce data at the breathtaking rate of 14 million gigabytes per second by 2024. It says,” Data Gravity Intensity, as measured in gigabytes per second, is expected to grow across 53 metros by a compound annual growth rate of 139% globally through 2024’’. This will warrant an additional 20,000 petabytes of storage annually. Organizations with giant footprints worldwide generate data at an overwhelming rate. Over 5 billion people interact with the data in this day and age. The number is expected to rise to 6 billion by 2025. Every person will come across data once every 18 seconds. Hence, the engagement with data will go over the roof.

Smart devices, including sensors and cameras, generate real-time operational data, which has to be processed quickly to be of value. The world is responding to the problem of data gravity by creating data centers and hybrid cloud solutions, scaling up the hyper-scale industry, and edge computing networks. The surge in Data Gravity is driven by the growing real-time insight needs, the democratization of Cloud Technology, and data generation.

The Challenge of Data Gravity

Here are some key challenges which are related to Data Gravity.

External Data and Diverse Sources

Traditionally, the data of a company was bound within the company's warehouses. The company establishment encapsulated the tools, technology, and people to make use of the data. However, the situation has changed due to the advent of external data. According to Constellation Research, over 60% of the most crucial company data will be external. Furthermore, the data sources in a modern enterprise are highly diverse: from comments on social networks to excel sheets. It is simply not possible to bring the data from various sources to a central data warehouse to be used for any value.

Size of Data and Time

The interview of Lin Nease and Denis Vilfort of Hewlett Packard Enterprise(HPE), has highlighted two significant challenges accompanying Data Gravity: the size of the data and time.

Data has become an indispensable stack for modern businesses, irrespective of their sizes. Enterprise Digitalization leads to colossal data generation for large firms with a global presence. The allocation of resources and architecture for edge computing doesn't prevent data generation.

The goal to achieve smooth operationalization through automation piles up more data. Hence, data production peaks at the operational sites of a business enterprise. The greater the size of the data, the greater the data gravity. For example, camera streams to monitor various business processes generate hundreds of megabytes per second. This large amount of data has to be processed immediately to yield value (CCTV data has value only in real-time to ensure on-site employees’ safety or measure live productivity). An effort to send the data to a central data repository to be processed later is counterproductive as it inhibits real-time decision-making and increases costs. Edge devices warrant instant communication and data processing. Time is of the essence.

Additionally, the dataset is too heavy to be moved to a core cloud. So broadly speaking, data gravity may be viewed as the interaction between the amount of data, the distance, and the processing capacity.

Analytic Workload

Data Analytics is the lifeblood of any enterprise of this decade. Data and analytic experts have to come up with the answers to essential queries which guide the growth of a company. According to an IBM-sponsored study carried out by Forrester in 2020, most decision-makers agreed that assembling data for analytics takes more time than it should. Data gathering is accomplished chiefly manually, which is work-intensive and time-consuming. Consequently, processing slows down, latency increases, and innovative effort has to give way to operational working

Furthermore, continuous shuffling of data gives rise to technical issues. The continuous moving of data in a hybrid cloud environment gives rise to unfathomable complexity on occasion. All these factors deprive companies of real-time and insightful decision-making needed for growth in a highly competitive environment.

Here are some responses to the study.

Impact of Data Gravity on Companies' Cloud Strategy

It follows pretty naturally that the companies have to spend a great deal more to purchase cloud services. Ever mounting expenditure puts considerable strain on the financial resources of an enterprise. Based on the Data Gravity Index report, Global 2000 Enterprises spend $2.6 trillion annually on IT Infrastructure & Networking. The data continue growing in terms of the big data set and the data pulled to the data set, and so does the need for more storage. Companies opt for Azure, Google Cloud, and AWS often to create a hybrid cloud strategy to limit the influence of gravitational forces and avoid vendor lock-in, but increases the level of complexity and chances of technical burdens.

Furthermore, a central cloud core has to give way to on-premise or hybrid cloud strategy. This is not to say that a central database is not required at all, but that edge computing has to be under more significant focus. Companies have to rethink the Cloud strategy and Enterprise Architecture with a Data Gravity-first approach to overcome the potential performance/technical issues, mitigate Data Management risk, and limit high financial costs (e.g. migration).

Final Words

To put it all together, I believe Data Gravity matters more than ever now, it could be the large challenge of the cloud industry of this decade. Enterprises have to confront the phenomenon of enormous data generation, processing, storage, and integration. The exponential increase in data creation is not going to reverse; instead, it will flourish, creating more significant challenges for the enterprises and their cloud strategy. Parallelly, the fast adoption of data-intensive applications, like Analytics, AI/ML, or Edge Computing, does not make the job easier.

Data Gravity will push forward new types of issues related to Security – Scalability – Data Velocity, and enterprise data architecture.

I hope you enjoyed reading this blog post! if you have any questions and comments, or just want to chat, please feel free to reach out to me.

The current state of the global economy and the endless crypto news have motivated me to finalize the draft of this article and share my thoughts on the emerging segment within FinTech that I call “ Asset Management as a Service” (AMaaS).

Definition & Introduction

I discovered this new segment a few months ago while I was mingling across Discord groups. Jumping in a brief chat with a user, who seems someone financially literate, led me to the bottomless crypto-asset diversification debate and my first introduction to “Asset Management as a Service”.

I could define it as the way to manage and invest clients/LPs’ funds without the need to have ownership and total access and/or control over the financial assets. On the opposite of the traditional approach, where Clients/LPs give up ownership and full control of their funds over a fixed time. It leverages multiple technologies like APIs, AI/ML, and blockchain to disrupt the legacy Asset Management space.

After a quick research, the recent data and metrics related to Asset Management blew my mind: this juicy industry sized $103 trillion in 2021, and retail portfolios represented 41% of global assets, or $42 trillion, according to BCG. The global crypto adoption & the pandemic have largely contributed to the spike in interest of retail investors for investment products. The US equity trading activity has doubled since the beginning of the pandemic, rising from around $18 billion/day (pre-2020) to around $32 billion/day in March 2020. And the number of crypto owners is expected to grow from 295 million to 1 billion by the end of 2022. The metrics from some companies operating in this space raised my attention to further dig in, like Kryll.io which operates across 112 countries and has managed to reach +$3.5 billion traded volumes in April 2022. Or Zignaly, the profit-sharing/copy-trading platform manages +$60M AUM, through API-linked crypto trading accounts, with +55K users up to date.

Assumptions & Market Drivers

Although the industry has seen some innovation over the past decade, it still remains fundamentally conservative given the nature of the business. In my opinion, the structural changes and new technologies applied to Finance will be key drivers of the emergence of AMaaS :

·        First and foremost, the global population is getting more educated about the Financial Market and the basics of Personal Finance. The multiplication of trading apps and neo-banks offering stock/crypto investing products depict this new generation's willingness to keep full control and ownership over their financial assets. According to The Motley Fool survey 2021, 73% of Gen Z investors and 66% of millennial investors own stocks, and unsurprisingly, 40% of stock investors aged 18 to 40 own cryptocurrencies. It demonstrates a growing interest in the investment space, Charles Swab talks now about the “Generation I(nvestor)”.

·        The decentralization of Investment Management knowledge outside of the banking/hedge fund industry, and the growing number of “amateur” investor communities across social networks largely participate in the change of the Asset Management landscape. The episode of Reddit’s traders vs HFs, or the anonymous Twitter user explaining step by step how to break Terra in Nov. 2021,  illustrates the capacity of some retail investors to research/act like "professional" investors. The community aspect and knowledge sharing play a crucial role in the Future of Asset Management: favoriting the "multiplayer" and "community-belonging" sentiment lead to higher engagement. A16Z addresses better the community-first social investing topic in the "Big ideas that fintech will tackle in 2022” post. Additionally, the myriad of FinMeme/StockMeme accounts genuinely outline the exponential factor of “Community” in the global financial industry.

·        Aforementioned above, trading platforms blossoming over the past years have upside down the traditional investment/trading market by granting commission-free, low-capital access, and fractional shares trading. The financial market consumerization and the democratization of retail investing activities play a subsequent stake in the emergence of AMaaS, as well as, has participated in the popularization of new investment instruments, like NFTs or cryptocurrencies. Technical innovations applied to Financial Services – like APIs, Blockchain, Bots, or AI/ML – are the main drivers of this new market. Quantitative/algorithmic investment strategies are getting more traction within the stock market in the past years (e.g. Two Sigma, Renaissance Technologies…), and automation has become a norm in the Finance industry. APIs can allow anyone to manage your brokerage/trading account with specific restrictions. Blockchain – especially smart contracts – acts as a safeguard/trusted-third party. Bots automize investment strategies while minimizing cognitive biases. Venture investments have started to hit the space, and startups have begun to stand out, such as the US-company Alpaca building a developer-first API for stock/crypto trading that secured +$70M from VC firms, or Zignaly developing a profit-sharing based copy-trading platform for digital assets raised +$50M.

The combination of these factors leads me to think of a future where: People would easily access  "plug and play" alpha-generating services within a few clicks. And anyone would be able to start "independent" Asset Management activities with access to enterprise-grade tools/ infrastructures.

Applications

From my initial investigation, the AMaaS landscape is growing over the days and attracting more retail and institutional investors. This space would include: marketplaces matching Asset Managers & customers, investment strategy creation/rental/sell/purchase platform, NFT-based asset management, DAO, wallet-2-wallet following, API-based trading infrastructure/web3 infrastructure, and enterprise-grade tools for retail investors/independent Asset Managers. Find below some interesting companies that I have spotted operating in this space.

I hope you enjoyed reading this blog post! if you have any questions and comments, or just want to chat, please feel free to reach out to me.