Should Your Fund Build or Buy AI?

Spend enough time inside investment firms and you'll eventually hear the same sentence.

"We should build this ourselves."

It usually comes after the team's first successful AI experiment. Someone connects a large language model to a few documents. It summarises a CIM. It answers questions. It feels surprisingly capable.

Buying software suddenly feels ordinary. Building feels strategic.

That's the moment many funds accidentally start becoming part-time software companies.

And somewhere between that healthy instinct and the actual decision to build, a lot of funds talk themselves into something expensive and slightly delusional.

Why investment funds want to build AI

The instinct is understandable.

Every fund wants to modernise. AI is reshaping how firms source deals, evaluate opportunities, conduct due diligence, and monitor portfolios. Nobody wants to be the fund that ignored the biggest technology shift in a decade.

Three things usually drive the decision to build.

The first is status.

"We're building proprietary AI" sounds impressive in an LP meeting. It signals innovation and technical ambition. Buying software doesn't generate the same excitement.

The second is control.

Investment firms naturally prefer owning critical systems rather than depending on vendors. Building feels like keeping everything in-house.

The third is the belief that our process is different.

Every fund has its own investment thesis, diligence process, and decision-making framework. That uniqueness feels like evidence that off-the-shelf software can't possibly fit.

Sometimes that's true.

Most of the time, it isn't.

While every fund's judgment is unique, the underlying work, extracting information from documents, comparing companies, drafting investment memos, tracking portfolios, and monitoring markets, is remarkably similar across firms.

That's infrastructure, and not just competitive advantage.

The build that always looks easy and never is

Here is the pattern, and we say this having watched it many times and built the hard version ourselves.

Building the first version is rarely the difficult part.

The prototype is genuinely easy. A sharp associate wires an API to a chat box in a weekend and it summarises a CIM. Everyone is thrilled. The decision to "build it ourselves" feels vindicated.

Someone inevitably says:

"It's basically just a wrapper."

That sentence changes everything. If it's "just a wrapper," why buy software?

A few weeks later, the questions start changing.

• Can we trust the answers?
• Where did this information come from?
• How do we evaluate accuracy?
• Who manages permissions?
• What happens when the model provider changes?
• Who maintains this six months from now?

That's when the real engineering work begins. Because production AI isn't a weekend project.

The thing hallucinates a number on a live deal. It cannot read the scanned PDF. It has no security model, so legal gets nervous. The model provider ships a new version and your wrapper breaks. You need evaluations to know if it is even accurate. You need someone to maintain it, monitor it, and keep it current, forever. And that someone is now not doing the job you hired them for.

What looked like a weekend project is actually a permanent product-engineering commitment: security, accuracy, evals, maintenance, model upgrades, support. That is not a project. That is a software company living rent-free inside your fund, staffed by people who would rather be investing.

The hidden cost of building AI

When funds compare "build" to "buy", they compare the API cost to the subscription price and conclude building is cheaper. That comparison is fiction. The real cost of building includes:

• The fully-loaded salary of the engineers, indefinitely, not just for the build.
• The opportunity cost of those Investment professionals spending time managing software instead of deals.
• Ongoing security reviews and compliance work.
• Continuous testing as AI models evolve.
• Technical debt that grows every quarter.
• The risk of losing institutional knowledge when key engineers leave.

Add that up and the subscription you balked at starts to look like the bargain of the decade.

Where your edge actually lives

No LP has ever committed capital because a fund had a better document scanner. They invest because they trust the people making decisions.

A fund's competitive advantage comes from things software cannot easily replicate:

• Investment judgment.
• Sector expertise.
• Relationships.
• Proprietary networks.
• Access to founders.
• Conviction around an investment thesis.

Technology should amplify those strengths, not become the business itself.

The best AI strategy for most investment firms isn't replacing judgment.

It's removing the repetitive work surrounding it.

Software does not create that edge. So the smart strategy is not "become a tech company".

When building AI does make sense

To be fair, because this whole piece is meant to be fair: a small number of funds genuinely should build. If you are a large, quant-style or highly systematic fund whose actual strategy is encoded in proprietary software, and you already run a real engineering organisation, then building can be the edge.

But notice the conditions. You already have the engineering org. The software is the strategy, not a support function.

For everyone else, building AI often becomes an expensive distraction rather than a competitive advantage.

Build where you're unique. Buy everything else.

The smartest investment funds don't outsource their judgment.

They outsource their plumbing. Buy the infrastructure that's already been solved.

Invest engineering effort only where your process is genuinely unique and defensible.

That approach keeps your investment team focused on sourcing better opportunities, making better decisions, and generating better returns, instead of maintaining internal software.

That's exactly the problem we built askRIA to solve.

Rather than forcing venture capital and private equity firms to become software companies, askRIA provides the AI infrastructure behind the investment process. It analyses deals against your investment thesis through Mind, cites every source, manages security, handles model updates, and continuously improves the system, so your team can stay focused on investing.

The best investment funds won't outperform because they built the most software.

They'll outperform because they kept software in its proper place.

Infrastructure should make your judgment more valuable and not compete with it.

Keep reading

• your fund's real AI strategy
• the 7 stages of a VC discovering AI
• we asked ChatGPT to run due diligence

*Remain as a great fund, and skip becoming the software company. Run your first deal free in askRIA*

FAQ’s

1. Should an investment fund build or buy AI?

For most investment funds, buying AI is the better choice. Building a secure, reliable AI platform requires ongoing engineering, maintenance, security, and governance that most firms underestimate. Buying purpose-built AI infrastructure lets teams focus on investing rather than software development.

2. Why are venture capital and private equity firms building AI?

Many firms want greater control, believe their workflows are unique, or see proprietary AI as a competitive advantage. While those motivations are understandable, much of the underlying infrastructure is common across funds and rarely creates meaningful differentiation.

3. When should a fund build its own AI?

Building makes sense when software itself is central to the firm's investment strategy and the organisation already has a dedicated engineering team capable of maintaining production AI systems over the long term.

4. What is an investment fund's real competitive advantage?

A fund's edge comes from investment judgment, relationships, sector expertise, proprietary networks, and a differentiated investment thesis, not the software it uses. The best AI strategy is to automate repetitive work while allowing investment professionals to focus on high-value decisions.