How Social Leverage Uses AI to Run a More Efficient VC Firm

Venture capital is a relationship business, but running a venture firm involves far more than meeting founders and deciding which companies to back. Investment memos, meeting notes, portfolio updates, LP communications, and internal research can consume hours that partners would otherwise spend building relationships and helping portfolio companies.

Matt Ober, a partner at Social Leverage, believes much of that operational work can be automated.

On an episode of How I Raised It, Ober explained how his firm has connected its existing software stack with Claude to build a more integrated investment workflow. By bringing together information from founder meetings, emails, CRM records, pitch decks, and portfolio companies, the firm can generate investment memos in minutes and use its accumulated knowledge to inform decisions.

But AI is only one part of the story. Ober's approach also reflects a broader philosophy about venture capital: build around relationships, stay disciplined about investment strategy, and make sure the firm's internal processes support the work that matters most.

For venture capital firms looking to operate more efficiently, his experience offers a practical starting point.

1. Build an AI Workflow Around the Tools Your Firm Already Uses

Social Leverage has been investing for approximately 17 years and, at the time of the conversation, was operating its sixth fund. The firm focuses primarily on seed-stage investments, particularly in fintech and vertical AI, with typical checks of $1 million to $2 million.

Over the years, the team accumulated a collection of software tools to manage different parts of its operations. Fund administration ran through Carta, meeting notes were captured with Granola, Affinity served as the CRM, and Superhuman handled email. The firm also had access to financial and operational data from its portfolio companies.

The challenge was that much of this information lived in separate systems.

A partner evaluating a potential investment might have met the founders several times, reviewed a pitch deck, received additional materials, and exchanged dozens of emails. All of that information could be useful in making a decision, but assembling it into a coherent picture took time.

Social Leverage began connecting these systems through model context protocol (MCP) integrations, allowing AI tools to work with information from the firm's existing software.

Claude became the interface through which the team could retrieve information, build workflows, and perform tasks using that context.

As Ober explained, the firm was bringing together everything from meeting transcripts and emails to investor documents and portfolio company data. Rather than treating every interaction as an isolated piece of information, the team could use that accumulated context to support its work.

This approach is important because it does not require a venture firm to replace its entire technology stack or build a proprietary AI platform from scratch.

The opportunity is to connect the systems a firm already relies on, then identify repetitive tasks that can be automated across them.

For smaller firms, where partners often handle investment research, portfolio support, fundraising, and administration themselves, even modest improvements can make a meaningful difference.

2. Turn Months of Investment Knowledge Into Minutes of Research

One of the clearest examples Ober shared was Social Leverage's investment memo process.

Traditionally, preparing an investment memo could take the team as long as two weeks. The process involved collecting information, reviewing materials, documenting the opportunity, and organizing the evidence needed to make an investment decision.

With its AI workflow in place, Ober said the firm could generate a memo in approximately three minutes.

The process begins with information the firm has already collected. A company might have gone through six meetings with the team, submitted a pitch deck and data room, and generated multiple transcripts and email exchanges.

Instead of starting from scratch, Ober can ask Claude to prepare an investment memo using that history.

The system can also draw on previous investments, allowing the team to compare a new opportunity against companies it has backed in the past. Ober uses it to ask questions such as whether a potential investment fits the firm's usual investment patterns and how the founders compare with those behind previous successful or unsuccessful investments.

That second capability is particularly interesting.

A venture firm's accumulated knowledge is one of its most valuable assets. Partners develop an understanding of markets, business models, founder behavior, and the factors that influence company outcomes through years of experience. Much of that knowledge, however, can remain scattered across individual memories, meeting notes, and old investment documents.

An AI workflow can help make that information more accessible when a new decision needs to be made.

Ober also said the system does more than agree with his initial opinions. He has found it useful for identifying risks and surfacing considerations he might not have thought about himself.

That does not mean an AI-generated memo should determine whether a firm invests. The value lies in giving investors a faster way to organize evidence, revisit previous decisions, and challenge their assumptions.

The partner still needs to assess the opportunity, question the founders, evaluate the market, and make the final call.

The goal is to spend less time assembling information and more time interpreting it.

3. Automate the Administration, Not the Relationships

For all the work Social Leverage has automated, Ober is clear about where the firm's attention should ultimately go.

He believes venture investors should spend their time supporting existing portfolio companies, meeting new entrepreneurs, and building relationships in person. Everything else should consume as little time as possible.

This distinction matters because venture capital is not simply an information-processing business.

A firm can analyze a pitch deck, review market data, and compare a company against its historical investments. But the decision to invest also depends on judgment, trust, founder relationships, and the ability to understand what a company might become.

Social Leverage has built its sourcing strategy around that human element.

Ober said approximately 99% of the firm's deals came inbound through founders, LPs, and other venture investors with whom the team has relationships. Rather than spending its time searching LinkedIn for undiscovered startups, the firm publishes its opinions through newsletters and other content.

The idea is to make its investment philosophy visible so founders understand what the firm believes in before approaching it.

The firm also offers founders a way to submit their companies through its website. It has incorporated voice AI technology that allows founders to have a conversation about their business, drawing on the types of questions the team would typically ask during an initial meeting.

These tools make it easier for the firm to process incoming opportunities without abandoning its relationship-driven approach.

There is a broader lesson here for emerging managers and established firms alike. AI can help reduce the operational burden of running a venture business, but it cannot automatically create the trust and reputation that generate high-quality deal flow.

The most useful automation is the kind that gives investors more time to develop relationships. 

4. A Strong Deal Flow Strategy Starts With Knowing Who You Are

Social Leverage's reliance on inbound opportunities is closely connected to its investment focus.

The firm concentrates on seed-stage companies, particularly in fintech and vertical AI. Its portfolio has included businesses such as Robinhood, eToro, Alpaca, and YCharts.

That history, combined with the partners' relationships across the startup ecosystem, helps founders and other investors understand the kinds of opportunities the firm is likely to consider.

Ober described the firm's name as reflecting the importance of leveraging its social network. Its sourcing model is built around relationships with founders, existing investors, and other VCs rather than indiscriminate outreach.

Content plays a supporting role. By sharing opinions and perspectives, the firm gives prospective founders a way to understand its investment philosophy before making contact.

This also helps reduce mismatches. Ober noted that investors receive pitches from companies that fall outside their stage, geography, or sector focus. Founders who spend time researching investors can avoid wasting effort on firms that are unlikely to invest.

For venture firms, the lesson is that better sourcing does not necessarily mean contacting more companies. It can mean making the firm's strategy easier to understand, building stronger relationships within its target markets, and creating a clear path for relevant founders to reach the team.

AI can support that process by organizing incoming information and helping investors evaluate opportunities against their stated criteria. But it works best when the underlying strategy is already clear.

Without that clarity, automation simply makes it easier to process more irrelevant opportunities.

5. Fundraising and Investment Discipline Still Matter

Ober's approach to operating a venture firm extends beyond technology. He also emphasized the importance of maintaining relationships with LPs and staying disciplined about the fund's investment strategy.

Social Leverage had raised several funds by the time of the conversation. Ober explained that the firm continued to engage new LPs even when existing investors had participated in previous funds.

The reason is straightforward: an LP's ability to invest again can change. Family circumstances, market conditions, and the timing of distributions can all affect whether an investor has capital available for a new commitment.

A fund manager cannot assume that a successful previous relationship guarantees the same commitment next time.

Ober also warned emerging managers against strategy creep. A manager who initially builds a reputation as a fintech investor, for example, risks undermining that positioning by suddenly pursuing unrelated sectors simply because a new category has become popular.

He argued for deep domain expertise and disciplined fund mathematics. Managers need to consider whether the size of each investment, the valuation paid, and the ownership they secure can contribute meaningfully to the fund's returns.

Writing a small check into a highly valued company may occasionally produce a good outcome. But if a manager repeatedly makes investments that cannot materially affect the fund, the overall strategy may become difficult to justify.

These decisions cannot be delegated entirely to AI. A system can help organize portfolio data, compare valuations, and make information easier to retrieve, but the manager remains responsible for the investment thesis and the economics of the fund.

Technology is most useful when it reinforces that discipline rather than replacing it.

The Future of Venture Operations

Social Leverage's experience points toward a practical evolution in how venture firms operate.

AI can help partners retrieve years of accumulated knowledge, prepare investment memos faster, compare opportunities against previous decisions, and reduce the administrative work involved in managing a portfolio.

The greatest opportunity is not necessarily to use AI to discover every promising startup or automate the entire investment process. It is to make the work surrounding investment decisions more efficient.

For emerging managers, this could mean building a lightweight workflow that connects meeting notes, CRM records, emails, and investment documents. For established firms, it could mean making institutional knowledge accessible across a larger team and reducing the time spent preparing internal research.

The starting point is to identify where valuable information is being lost across disconnected tools and where repetitive work is taking time away from founders and investment decisions.

Ober's central philosophy is that the firm's technology should serve its relationships, not compete with them.

In a business where judgment, trust, and access remain important, the advantage may belong to firms that use AI to become more focused on those things, not less.