Dmitry Kaminskiy in Fortune Arabia: Innovation Gap Between AI and Financial Market Infrastructure
- Deep Knowledge Group
- May 19, 2023
- 3 min read
"Investment strategies in the current landscape require sophisticated, AI-driven and data-informed approaches"
AI has advanced to a high level of sophistication, yet the financial market infrastructure remains relatively underdeveloped. Therefore, there is an immense opportunity for progress within the world of finance when it comes to investing in cutting-edge technologies.
Struggling to keep up with 21st century technologies, outdated consensus approaches continue to dominate investment and financing decisions in the AI industry. As a result, vast amounts of potential funding for innovative companies remain untapped, leaving both investors and innovators at risk of missing out on potentially lucrative opportunities.
The world’s biggest investors and largest holders of wealth exhibit a prudent, conservative approach towards investing. They primarily focus their attention on markets that are de-risked, liquid and highly tradable, making venture capital firms the sole riders in the arena of AI financing–these entities can hold longer investment periods coupled with higher levels of risk.
Investing in the AI industry? Leverage AI for due diligence
Investment strategies in the current landscape require sophisticated, AI-driven and data-informed approaches to keep up with rising complexities. Investing in AI technology is best done using AI itself. Investing ‘with’ AI can range from target identification and analytics to forecasting, benchmarking, due diligence and de-risking, all while using AI systems that are capable of executing these investments accurately.
Sifting real AI from hype
Early investors in AI have the potential for major financial rewards. For those considering AI ventures, it’s important to conduct due diligence and ensure the investment is truly rooted in science-driven technology. Otherwise, they may simply be buying into an inflated buzzword rather than realising tangible returns.
For instance, FinTech experienced a boom between 2016-2019, and the returns for investors were remarkable. However, with FinTech now becoming somewhat ‘standard technology’, savvy investors have shifted to complex domains at the convergence of science and technology—something that was scarcely seen roughly five years ago. By investing in these forward-thinking fields, individuals as well as institutions can generate differentiated returns from their ventures.
To evaluate an AI startup as an investment target, investors must conduct extensive due diligence specifically adjusted to the AI sector rather than using traditional assessment metrics.
Some savvy investors are already safeguarded from exaggerated claims with the aid of advanced AI-powered due diligence solutions that provide semi-automation to reduce risk.
Dmitry Kaminskiy, General Partner, Deep Knowledge Group
AI startups seek to revolutionise and shape the future, so naturally they must hold themselves to a much higher standard. Without scientists or engineers on board, these startups would not be considered true AI players.
The next big asset class
AI has the potential to revolutionise investment opportunities. By taking a step towards making it a new asset class on its own, regardless of risk or liquidity preferences, we can open the door for both individual and institutional investors to benefit from its commoditised products and services. Therefore, we would need to create dedicated tradable assets such as indices and derivatives that offer low risk exposure in high value tech sectors.
Establishing AI as a new asset class would unlock trillions of dollars in capital and turbocharge the AI ecosystem, allowing us to create an immense impact on societies and economies. These advances will enable a brighter future while addressing some of our greatest challenges with unprecedented speed.
Investment caution
Investors must be prudent with their deals, taking strategic steps to ensure success, such as leveraging data science and AI for validating potential opportunities, implementing technological forecasting systems wherever possible to improve the accuracy of predictions, and utilising modern InvestTech solutions instead of relying on antiquated models. By arming themselves with these tools, investors can reduce risk while enhancing their liquidity and stability.
With the AI sector growing at a rapid pace, investment opportunities have become plentiful. To make these investments accessible and feasible to financial institutions, retail and conservative institutional investors, innovative approaches must be adopted to optimise decision making through AI and data science.
As the world of AI progresses rapidly, investment approaches and financial infrastructure have been left in a state of constraint. There is an urgent need to address these limitations so that humanity can reap the potential rewards from this revolutionary technology sooner rather than later.
Read the full article in original Arabic at the Fortune Arabia website.
The gap between AI innovation and financial infrastructure is a fascinating challenge. Technology can move incredibly fast, but systems often need time to adapt, similar to how new features and strategies evolve in nulls brawl ios.
When you say the infrastructure is lagging, I immediately think about operational “fit” — the best model doesn’t matter if it can’t be integrated into how decisions get made day to day. It’s like personal style systems: you can know your soft summer palette guide, but if it doesn’t translate into what you actually buy and wear, it stays theoretical. In finance, what’s the equivalent of that last-mile adoption step?
The conservative-capital point resonates — a lot of “innovation” dies because it can’t be expressed in the formats committees already understand. Funny enough, it reminds me of how people chase a familiar aesthetic when experimenting with creative tools, like a Ghibli-style photo transform, because it’s a safe reference everyone can judge. In investing terms, how do you avoid anchoring diligence on the most legible narratives?
I like the framing that investors want de-risked, liquid markets, but that preference also shapes which AI companies get built in the first place (optimize for what can be “packaged” and measured). I’ve seen similar dynamics in tool directories — hrefgo comes to mind — where visibility nudges products toward whatever’s easiest to categorize. What would an AI-native diligence process measure that today’s IC memos usually ignore?
The gap between model sophistication and market plumbing feels real — especially when reporting cycles and audit trails still move at human speed. Side note: when I’m trying to sanity-check “time saved” claims I end up using this site to do quick math, and it’s funny how often the practical constraints dominate the theory. In finance, is the missing piece better data standards, or faster settlement rails?