Building Strata as an AI-Native Investment Firm
Most conversations about AI and investing focus on what investors should buy, which companies will benefit, which markets will change, and where value will accrue. We are equally interested in a second question:
How will AI change the way an investment firm itself operates?
When we founded Strata, we started with a blank sheet of paper, and we did not need to retrofit new technology onto decades of accumulated systems and processes. Instead we started with a more fundamental question: if we were building an investment firm today, which activities would still require human judgment, and which could be performed more effectively by technology?
We believe that the core investing job depends on judgment, trust, and relationship building. Technology can and should be a tool to help investors improve sourcing, picking, winning, and portfolio support. Over the course of our 30+ years of investing and building, we have seen many approaches to how firms leverage technology. The Strata approach focuses on how we can use technology to automate the low-value work that investors would otherwise perform themselves, enabling them to focus on high-value work. Every application is evaluated through a simple lens of “does it reduce meaningful work, improve the quality of our investment decisions, or help us better serve founders?” If the answer is no, we generally choose not to build it.
Technology Must Earn Its Place
Technology that merely creates another dashboard, workflow, notification, or reporting requirement often adds more friction than value. It is now remarkably easy to build something that looks impressive but just amounts to creating more busy-work for humans. Generating more output is not the same as improving the investment process.
At Strata, every AI workflow must satisfy four principles:
It should replace repetitive / labor-intensive work a person would otherwise have to perform, enabling humans to perform higher-value tasks
It should contribute directly to finding, evaluating, or supporting exceptional companies
It should not create more work than it removes
Humans ultimately control / review conclusions and communications
These principles have led us to build narrower systems built around automating real work.
Research That Improves Judgment
Our initial application of AI was to use technology to improve our thesis development / research process. As we develop investment theses, we use AI-assisted research to map markets, examine adjacent categories, identify relevant companies, compare business models, and surface evidence that may contradict our initial view.
Our research agent takes into account prior work we have done along with the most current view of a specific market to synthesize an informed view on a market. The agent understands our buy-box and will test potential opportunities against our criteria. Once it finds a company that fits, we engage in a dialogue with the agent to identify areas of opportunity or concern. This dialogue helps improve the agent’s understanding of our lens with the goal of making it more effective over time. Moreover, we are particularly interested in using the agent as a source of productive disagreement. If we believe a market is attractive, the system should help identify why that belief may be wrong.
Making Sourcing More Systematic
The best investment opportunities are rarely found by sending the largest number of outbound emails. Successful sourcing requires identifying the right company, understanding why it may be relevant, finding a credible path to the founder, and reaching out with a point of view that demonstrates genuine interest with tangible ways we can help them scale. We use AI to reduce the work around that process and give us greater leverage on our time.
Once we identify a company that may fit our strategy, our sourcing workflows can help us:
Assemble relevant information about the business and its market
Identify mutual relationships with the founder or management team
Determine which member of our network is best positioned to make an introduction
Propose ways that we (including our advisory network) can add value to the company
Draft outreach based on a specific reason for contacting the company
Monitor meaningful developments over time
Human review remains essential. Founders can distinguish thoughtful outreach from automated personalization, and sending more messages is not our objective. The value of the system is that it helps us approach a smaller number of companies with better context and remain engaged with them over time.
Many of the strongest investment relationships begin years before financing. Maintaining that relationship manually across hundreds of companies is difficult. Technology can help us remember what mattered, notice when circumstances change, and reconnect for a substantive reason. The relationship itself is still critical and at the heart of our process.
Scaling Relationships Without Automating Them
Investing is a relationship business, and we don’t expect that to change.
Founders choose investors based on trust, experience, and reputation. It would be impossible to replicate that through an agent.
Our relationship workflows are intended to help us be better at managing our connections. They can help us:
Identify the strongest connection to a person or company
Prepare for a meeting using prior interactions and relevant developments
Surface relationships that have gone quiet
Build thoughtful meeting lists when traveling
Review conference attendees for relevant founders, operators, LPs, and co-investors
Recognize reasons to reconnect including offering ways we can add value
Share relevant news or insights with the appropriate people
The objective is not to optimize the number of touches. It is to make each interaction more informed and to ensure that important relationships do not depend entirely on someone’s memory.
A Small Team With Greater Leverage
The traditional response to increasing investment activity has often been to add people and process. More opportunities produce more analysts. More portfolio companies produce more reporting. As the organization grows, information becomes harder to share.
We believe the most exciting aspect of AI is to enable a small team to do much more. We see significant benefits in reducing the layers between each investor and enabling information to flow faster with less of a filter.
Removing that work allows a lean team to spend more time on the parts of investing where time and attention genuinely matter.
Building a Better Investment Firm
Our ambition is not to build the investment firm with the most agents, but rather to build a firm where investors can gain the most leverage. If AI allows us to spend less time assembling information and more time exercising judgment, it is useful.
The investment firms that benefit most from AI will not necessarily be those with access to different models or tools. Most firms will have access to similar technology. The difference will come from how thoughtfully each firm redesigns its work: what it automates, what it preserves, what it measures, and where it insists on human judgment / interaction.