Are venture capital investors turning to Artificial Intelligence to find and source deals?
As per a recent Crunchbase report, US-based VC firm SignalFire and European fund EQT Ventures have developed their own AI platforms to analyse and vet investment opportunities. EQT’s AI platform, called Motherbrain, was used to source its portfolio companies and played a role in more than US$100 million of its ~US$900 million total invested since its first fund opened in 2016.
No doubt, new-age technologies such as AI have conquered almost every industry in the world, and venture capital is no exception. The tech adoption has further accelerated thanks to the spread of COVID-19. As the virus mutates sending shockwaves in many parts of the world and making physical due diligence almost impossible, VCs turned to new ways to make sure the process goes uninterrupted.
But could AI be a game changer here? What will be its roles in the space? Is this going to be the future? Will more VCs come forward to adopt this technology in the coming years?
We threw these questions at several notable investors and industry veterans in South and Southeast Asia.
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Below are their edited comments.
Dave Ng, General Partner, Altara Ventures
AI and Machine Learning works well when you have high quality data set to operate with. And the larger and more relevant the data are, the better in terms of outcome you could get.
Think of data as being the fuel that powers AI, which is the engine. Hence, when the fuel is cleaner and high in quality, your engine will go faster.
In recent times, AI/ML has been applied in many fields, including investments. It works better in an environment where you have meaningful volume and veracity of information.
Naturally, this means more applications of AI/ML and data science in areas like public equities, hedge funds, quantitative strategies.
In the realm of venture capital, the notion of applying AI is no doubt attractive. However, the question is where could AI be truly impactful? Because within early stage venture investing, very often you are faced with incomplete information and even a lack of data.
Hence, to strictly or even heavily rely on AI for your decision making process would be a stretch. For that to happen, we would be talking about Artificial General Intelligence (AGI), which is the holy grail in the AI field.
Think of JARVIS in the Marvel superhero movie Ironman. But today, we are still quite some distance in getting there. This is why many would say venture investing is both art and science.
While you could crunch numbers and analyse operating metrics of a business with technology, you can’t read the founders’ behaviours and qualities using a machine — their characteristic, mood, attitude, ethics, level of grit, ability to persevere etc.
And better still, it often takes an experienced former entrepreneur and business builder to really empathise and grasp the nuances of another startup founder and her/his business.
Where I think AI/ML and Data Science could be helpful would be in a few key areas such as (1) top of the funnel sourcing, (2) market signals processing, (3) trends analysis & pattern matching, and to a certain extent, and (4) predictive extrapolation. And many of these are still predicated on how good your data set is. You can imagine this is harder to do in emerging markets and early ecosystems.
I think AI/ML and Data Science are already increasingly being applied in many fields, including investment. Hence, regardless of COVID-19, this is a trend that will continue to pick up. I really see them as being independent from the pandemic.
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At Altara Ventures, for example, we apply technology to many of our processes and activities. We take a data-driven approach to our investments and constantly fine-tune our tech stack to enable our venture activities.
Justin Nguyen, Partner, Monk’s Hill Ventures
As former tech entrepreneurs working among AI and now venture capitalists investing in AI, our natural inclination is to always be on the lookout for ways to apply technologies to improve the status quo.
We are constantly asking ourselves “How do we apply technology for an advantage in the investment process?”, with one simple goal in mind: let humans do the relationship and intuition parts and let’s see how far we can push computers to do the rest.
Perhaps the adoption of AI in investment will hasten a bit during COVID-19, but not significantly, especially among the early-stage funds. Somewhat ironically, tech VCs are sometimes some of the slowest to adopt technology in their own practice. This is possibly best exemplified by the sheer number of firms that run their deal sourcing and investment process with little more than a handful of Excel spreadsheets.
In fact, we find ourselves a bit of an outlier among our peers in Southeast Asia, having invested in systems and data analysts from the very beginning. And we’re already evolving — earlier this year, we overhauled our customer relationship management (CRM) system to help us pool our individual networks.
Our current system constantly analyses billions of data points to inspect, measure, and surface relationships in our network, giving everyone at the firm a 360 degrees view and a shared history of our collective Rolodex, eliminating the silos that were previously plaguing us.
There’s a lot more to be done and certainly ways to go to realize our goal of letting humans do the relationship and intuition parts and pushing computers do the rest, but we’re well on our way.
Edward Tay, CEO, Sistema Asia Capital
AI has a phenomenal role to play in VC and private equity, albeit to different degrees. In terms of sourcing, VCs have much to gain from tapping on domain knowledge from seasoned in-house practitioners, such as Sistema, which has been investing since 1993.
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Key advantages are to reduce the work and headcount and increase the ability to curate higher quality of startups across cyclical business cycles as it removes innate human elements.
In terms of portfolio management, it improves the portfolio management efficiency and speed as it automates and focuses valuable management energies onto resolving key issues residing within the portfolios, and also the optimum time for exit, as the startups navigate the ups and downs in the post covid world.
This subtle advantage is further amplified for bigger funds that operate across different territories and countries. Sistema has fund management experiences and portfolios across major economies globally.
Compliance will be key as we will still need high calibre professionals and employees to ensure that results are consistent in quality of data sets, and ensure proper processes are in place for the extraction of such data — many of which are proprietary in nature to the organisation.
James Lee, Managing Director, Vertex Growth Fund
With the increasing availability of data — private market data and alternative data stream — there is a growing potential to leverage AI to derive signals and patterns that can inform and complement investment decisions.
At Vertex, we built an internal platform that supports our deal sourcing efforts as well as business development and cross-border partnerships between our portfolio companies and strategic corporates.
From our experience, the challenge has shifted from availability of data and AI tools to identifying the most relevant business challenge statement. With AI, this has become more streamlined and efficient, therefore helping us better identify investment opportunities.
In the near term, we see AI being a powerful decision support tool as part of our investment process. The ability to find patterns in the data universe to signal promising companies or emergent sector trends can help with deal sourcing. Graphs and data analysis can also be applied to portfolio value creation use cases.
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We are not yet concerned that AI will replace human investment professionals, but recognise that it can be a great complement to the investment decision making process.
The acceleration of digital adoption by both individuals and enterprises due to COVID-19 will only further expand the data universe and increase the scope for AI application. AI adoption will naturally become increasingly common in the industry as it becomes an indispensable tool and a competitive advantage for firms.
Nitin Sharma, Founder of FirstPrinciples VC
Quantitative VC has been talked about for over a decade; firms like Correlation Ventures, Google Ventures and Social Capital were part of the first wave of funds using data science to track myriad of data sources and pick investments.
There are now many more funds building internal data teams and tools, and using AI to guide not only sourcing and tracking of new startups, but also diligence and post-investment support as well.
However, this is still a phenomenon primarily restricted to a subset of firms, and only in certain very dense and mature ecosystems where VCs are competing for any small advantage in deal flow. When it comes to India or SEA, I am not sure many investors are relying on advanced data signals yet.
VC, especially here, is still very much a people business driven by intuition and relationships. In the COVID-19 world, VCs everywhere have become used to completing the deal cycle online, and the trend towards applying AI in investing decisions has probably accelerated.
Also, certain types of financing decisions, such as ARR-based SaaS financing, can be more easily automated and linked to metrics.
Overall, however, early-stage equity investing will always have an element of subjective judgment (storytelling, founder chemistry, grit, etc.) that can’t be quantified.
Doing quantitative VC right also requires a serious investment of time and talent, which most VCs in the region are not ready for.
Wing Vasiksiri, Managing Partner, iSeed SEA
I’m a big believer in AI helping VCs in the sourcing process but am less optimistic about AI helping VCs in the picking process.
In the sourcing process, there are several ways this can be streamlined especially if a VC has a specific thesis they are looking to invest in.
For example, if I have a thesis around a specific type of founder with a certain background, I could build a software layer on top of this to source them.
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However, I believe that the picking process is very human and is not scalable through software. For us, picking great founders means getting to know them on a human/personal level and this can’t be done through software — we’re making bets on people rather than products and companies.
COVID-19 has increased the adoption of software tools globally. However, unless a fund has been software and AI first from the start I think it is unlikely that COVID-19 has accelerated the use of AI in investments.
During uncertain times, humans tend to become risk-averse and resort to what they know and what has worked in the past, rather than seek a new investment and deployment strategy.
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