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Eugene Mikhaylov, CAIA
GP @AAL VC | investor in Seed->Series A startups in ai, infra, devtools
Eugene Mikhaylov is a co-founder and General Partner at AAL VC, a venture capital firm based in Montreal.12 As a CAIA-certified investment professional, he brings over 7 years of hands-on venture capital and startup experience to his role.26
Professional Background
Eugene leverages his expertise to invest in cutting-edge technology startups, with a particular focus on:
- Deeply-technical products using AI and other frontier technologies
- Companies showing first revenue
- Sectors such as AI, VR, and DevTools3
Investment Strategy
AAL VC, under Eugene's leadership, typically invests in the following range:
- Sweet spot: $200,000
- Investment range: $100,000 - $400,0001
Team and Fund
Eugene co-founded AAL VC alongside Roman Chernin and Dmitry Stepanov, who serve as the fund's General Partners.5 The fund is reported to be a $10 million micro VC fund.6
Location
While AAL VC is based in Montreal, Eugene himself appears to be located in Vancouver, British Columbia, Canada.4
Eugene Mikhaylov's experience and expertise make him a notable figure in the Canadian venture capital landscape, particularly in the realm of frontier technology investments.
Highlights
I’m in LA this week. Ping me if you’re here or know someone among founders, VCs I should meet🙏
Do startups (could possibly) have any advantage in ML? That question comes up over and over again, but the answer is still unclear.
We all know that data access is expensive. The costs of processing and moving data as well as maintaining its "freshness" are outrageously high. The latest Google's acquisition of millions of health records raises additional questions about ways for startups to compete with FAANG and the likes (that arguably have all the data there is).
The standard recipe that is out there is to avoid competition with giants by focusing on leveraging ML in particular niches and specific domains (like unsexy markets). Those could be good opportunities but they don't translate to great success.
Why? I see 2 fundamental obstacles that prevent startups from going along the famous "start small - grow big" path: 1/ backwards economy of scale of data 2/ unique datasets and models for every customer
How to fix this? I make my bet on "less data approach". I.e. new algorithms that don't require as much data to train as classic ML/DL models. #startups #ml
