There is seemingly a constant noise in the business press at the moment around AI, ML, VR, AR etc, and it’s often hard to see the wood for the trees. One of the perks at Freshminds is the number of conversations we have with various professionals including certain changes to industry. We can, therefore, end up acting as something of a barometer for where certain trends are going to appear next.
In one instance during conversations towards the end of 2017 and throughout the beginning of 2018 that have indicated a specific example of how AI/Machine Learning is disrupting the Venture Capital and Private Equity market; a sector that Freshminds has always had a close affinity with. True to form, we have indeed begun to see more articles delving into this topic, indicating a rapid growth of this trend.
This use of AI/Machine Learning in VC/PE is in many ways nothing massively new. However, we are seeing more practical examples where clever tech has been able to pick up on information that would otherwise have been overlooked. The technology surrounding this and the sheer quantity of data available has also advanced to such a degree that AI is becoming one of the most practical and necessary ways of coping with the volume of information this is now available.
How is it being used?
It essentially boils down to using of AI to pick deals. Its ability to logically apply a myriad of different filters to massive data sets means that it can assess a huge amount of prospective deals and come up with 100s of opportunities that fit an investment thesis. This also means that there could actually be suitable timing rather than the 10s of investment opportunities that a firm could comparatively research and initially screen in a same amount of time.
A VC firm can say that it wants to see companies that have just hired a certain type of developer, or registered an interest in ‘y’ technology. Or, further down the pipeline, have hired ‘z’ amount of sales people etc. Thus if company ‘x’ is fitting these activities / exhibiting the right behaviours then it becomes a target and enters the other stages of investment for further scrutiny.
Obviously, technology has been doing similar things on more basic levels for decades. But a lot of that has, by today’s standards, been quite heavy handed data analysis and a lot of it tied to stock markets or bigger LBO type opportunities, so this is less useful for the VC / low-cap end of the marketplace. The sheer quantity of variables that is now searchable; everything from human capital, through to the minutia of technology application, means that for the first time the data firepower at the disposal of those trying to find deals is quite extraordinary.
At Freshminds we have met with more and more companies creating their own proprietary market scraping technology to aid them in picking up on those diamonds in the rough that they would otherwise miss - even if it takes them into brave new markets. Though, that said, it is perhaps a little too early to judge the efficacy of this computer-based research and origination. To date, there have been relatively few transactions as a direct result of this approach, but the number is increasing quickly. This new approach has the potential to completely change how VC/PE firms operate and go about discovering their next investment.
How does this affect jobs in this industry?
Strangely this rise in AI has, in a lot of ways, had the exact opposite effect to that which we normally expect on the jobs market.
As the wider Financial Services world began to see a couple of years ago, AI can often mean the end to multiple jobs. Where simple data entry is automated, algorithms can replace hundreds if not thousands of employees. However, in VC/PE, the fact that clever computers are picking up on prospective deals doesn’t mean that they can do the subsequent screening and filtering of the deals.
AI will no doubt begin to make headway in the overall investment gamble in other ways and will begin to make a bigger and bigger impact in investment committee decisions around the globe.
However, in the meantime, what we have seen is actually an increased appetite to hire analysts and associates at the more junior end of the spectrum (previously a real rarity in VC/PE).
Where computers are picking more deals, firms need more people to manage the influx of information and potential opportunities. The need for excellent candidates who can balance the EQ and IQ necessary to screen the investment is still very much evident. Also, there is an added element of bringing in more junior talent to get to grips with / have prior knowledge of the technology required to run the AI-driven searches of the marketplace.
VC and PE have already started to treat the engineers and data scientists differently and the power no longer lies quite so exclusively with the investment teams. It’s a trend that will only increase as Machine Learning and AI play an ever-increasing part in the investment lifecycle.
Alongside the fact that it hasn’t negatively affected jobs in the space, there are some other potential benefits that are perhaps a little more surprising. In essence, there is beginning to be more of an equal playing field and the slow erosion of the innate nepotism in the sector. By its very nature, VC/PE relies on smart incredibly well-networked people with their ear to the ground. With the best will in the world this will likely result in a lot of friends working with friends, and people without an ‘in’ could really struggle to access the funding and networking circles that are necessary to significantly improve their ability to succeed.
With AI calling the shots, potential deals are picked purely on their merits as a probable future success. The true unknowns, possibly sat in bedrooms with an idea in any far-flung corner of the planet, have the ability to be spotted and helped by VC and PE firms’ way outside of their normal ability to interact with.
You might ask ‘so what’ to this particular perk of AI, but it means that more and more entrepreneurs can have a voice and a better start to their investment journey. As such, and to end on a positive note, the increased use of Artificial Intelligence and Machine Learning in Venture capital and Private Equity could be heralding a new and much meritocratic approach to the world of the start-up.