AI-Driven Venture Capital
Funds use a data-driven or AI-led approach to identify and provide capital to early-stage businesses with return or growth potential.
AI-driven venture finance funds evaluate multiple data streams of data to identify companies with potential for high growth, finding companies that may be overlooked or undervalued by standard venture capital.
AI-Driven venture capital vs. regular venture capital
AI-driven firms market their fund compared to standard venture capital, whose deal flow is based on their networks and relationships to specific geographical startup ecosystems and university connections. As the world gets more detached from the Bay Area as the epicenter of venture-funded growth, an opportunity has emerged to find a different mechanism to identify emerging growth companies.
Additionally, AI-Driven Venture Capital can claim to address the inherent bias that may exist in human-formed networks that are not open to diverse founders (but must then practice a data-driven and decision strategy that delivers better than industry standard results).
AI-Driven venture finance is not automated
AI-driven venture finance provides decision support to fund managers but is not an automated process. These firms use the data exhaust of companies – social media interactions, credit card data, sales data, and other publicly identifiable data sets to develop what is called deal flow: the list of companies that a firm should consider for investment.
Example Firms
Fly Ventures | In Reach Ventures | EQT Ventures | Hatcher + | Hone Capital | Deep Knowledge
Is AI-Driven VC the right capital for your business at this stage?
COMPANY PERSPECTIVE
Benefits
- May perform better on valuation based on fundamental unit economics vs. pedigrees
- For investment groups that also use AI to guide companies - can help optimize decisions
- Ideally more data-driven board room conversations vs. most powerful person in the room
Challenges
- Narrative may not be as highly valued by these firms
- The data-driven approach may not change the diversity makeup of deal flow without specific efforts to engage these communities
- Data-driven approaches used to find deals, meaning valuations offered may be substantially lower than industry norms if there are no other investors pursuing the company
- Innovative concepts or business models with no similar precedents may not receive opportunities for funding
Key Performance Indicators
- Pre-money valuation relative to normative VC
- Post-money valuation relative to normative VC
CAPITAL GIVER PERSPECTIVE
Benefits
- Track fact-based market trends before meeting companies
- Access to previously "invisible" startups
- Efficient screening across more metrics than manual screening
Challenges
- Some human aspects of evaluation are difficult to codify
- Early stage startups act as complex adaptive systems with unpredictable elements, frequent changes in data
- If all funds use similar data sources, the quality of analysis may not yield unique deal opportunities
Key Performance Indicators
- Internal rate of return % (IRR)
- Net present value (NPV)
When it Works Well
Benefits companies with data exhaust
Companies in sectors like e-commerce and B:C marketplaces may be better targets for AI-driven investment where third party data can be purchased, ingested, and analyzed. These sectors have purchasable datasets. If your company is performing better than your comparable companies, your firm may be discovered and perform well with AI-sourced deal flow.
Generates undiscovered deal flow
VC firms are looking for the best companies for the best deal, and often in hyper-competitive sectors or ecosystems, VCs may be priced out of deals. AI-driven sourcing works best when companies are discovered that have not yet been aggressively courted by leading VCs.
Improves diversity track record
AI-driven deal flow processes are fairly new. If these pipelines are able to increase sourcing and location of company founders outside of the standard Bay Area and top city hubs, this may result in more women and people of color getting funding – but these stats may not appear for a few years. Time will tell if deliberate network development through regular VC will improve these odds vs. standard VC.
Networks as strong as regular VC
From the company’s standpoint, make sure you are not trading down to a less networked venture capital firm. The value of VC still lies in their relationships with other firms that may co-invest or invest in later stages, or banks that may support a public event, or companies that may acquire your company. Do the same amount of reverse due diligence in the VC’s portfolio to ensure the firm can provide these benefits.
Challenges to AI-Driven VC
The narrative often outweighs the numbers
Companies that go on to raise Series A, B, and C and beyond tend to have powerful narratives that attract later stage investors. While numbers matter, the most successful tech companies tend to also rely on big visions of the future – Tesla to Apple to Amazon. This is especially true in the earliest years before major exponential returns kick in, and before profits flow. A data-driven approach in VC may be as unsuccessful as a value-driven approach in public markets investing – over focused on benchmarks and intrinsic evaluation, and not enough on the convergence of technology and power of founder-led storytelling.
AI can’t manage relationships
Humans are often better than AI at detecting the strength of team dynamics and building relationships with founders to help them make strategic decisions and navigate uncertainty. The actual value of AI-driven selection and operational support may end up not providing as much value to the companies in the portfolio, who may benefit from old-school relational support.
No AI is bias-free
Humans still have to factor how much value to give to parameters, and simple decisions about schools and prior fundraising rounds may still serve up the same deals that perform well in a standard pipeline – Stanford computer science engineers that have been through YCombinator.
Trends in AI-Driven VC
Rise of more AI-driven funds
AI-Driven VC is a trend within VC, with over 25 funds now claiming the use of AI in different ways. If the trend continues, your company’s data exhaust from credit card receipts to social media engagement to competitive intelligence may factor into your valuation in the future.
AI in VC operations
Some funds go beyond using AI to screen companies and provide assistance in the development and data operations for their portfolio companies and advice on how to scale AI-driven companies.
The decline of the pitch?
Industry experts predict that the traditional pitch experience will significantly shift by 2025 and tech founders will need to give investors access to AI-enabled models and simulations as traditional pitch decks and financials will be insufficient.
Before You Consider AI-Driven VC
- Do you have evidence of customer demand that is substantially better than the competition at your stage?
- Do you have an observable set of data?
- Are you planning on fundraising and are you actively seeking regular VC in order to compare terms?
Exploring AI-Driven VC
- You can find a number of excellent roundups of AI-driven VC if you feel your company demonstrates strong traction.
- Always run your own investment process in a proactive manner, do not wait for investors to find you.
Discover Alternative Capital
AI-driven VC is an emerging trend, but there are other capital types as an alternative or pair and combine at different stages of your business. Consider these alternative capital sources or explore our Capital Library.
