Build vs buy is the wrong question for AI research

Two bad options, and the third one most teams overlook

Most investment firms evaluating AI for research frame the decision as a choice between two paths. Build it in-house or buy it from a vendor. Both paths are real, both have champions within most firms, and both face a serious problem that tends to stall the debate before it reaches a conclusion. The argument runs in circles because each side is right about the other's weakness. What neither side tends to notice is that the question itself has been overtaken by events.

Building in-house is appealing because it promises a system shaped exactly to your process. For a function whose entire value rests on a proprietary method, that fit is not a luxury. It is the whole point. The reality of building, though, is harder than the promise. It means hiring or diverting scarce engineering talent away from work that may sit closer to the firm's edge. It means standing up data infrastructure, sourcing and cleaning the filings, transcripts, and market data an agent needs, and keeping all of it current. It means maintaining models as they evolve, which in a field moving this fast is not a one-time cost but a standing commitment. And it means owning the whole thing indefinitely, long after the people who built it have moved on.

Analysts describe a build-deploy-run lifecycle that is materially more complex than that of standard software, because an agent that acts on your behalf requires governance, monitoring, and upkeep that a dashboard never did. A dashboard that breaks shows you a stale number. An agent that drifts produces research your team may act on without realizing the ground has shifted beneath it. That difference is why so many in-house projects deliver a promising prototype and then stall. The demo works. The problem is everything that comes after the demo: the monitoring, the version control, the audit trail, the slow accumulation of maintenance that no one budgeted for. The prototype was the easy ten percent.

Buying from a vendor solves the maintenance problem and trades away the fit. You get something working quickly, supported, and kept current by someone else. But it does what the vendor designed it to do, in the way the vendor designed it to be done. For a generic task that is fine. For a function whose entire value rests on a proprietary method, a tool that applies a generic one is a poor match. Teams end up bending their processes to fit the software, which is precisely the wrong way around. The method that made the firm distinctive gets quietly sanded down to whatever the platform supports, and the edge erodes one workaround at a time.

There is a deeper cost to the buy path that rarely shows up in the evaluation. When you adopt a vendor's method, you also stop developing your own. The research process becomes something you receive rather than something you refine. Over years, that is how firms lose the very thing that distinguished them, not in a single decision but in the slow drift of letting someone else define how the work is done.

The framing itself is the problem. Build versus buy assumes you must either own everything or accept what you are given. It treats fit and maintenance as a trade-off you are forced to make, when the only reason they were ever linked was the cost of building. Remove that cost and the trade-off disappears.

There is a third path that collapses the choice: a platform you configure yourself. The infrastructure, the data, the maintenance, and the governance are handled for you, the way buying delivers. The logic each agent follows is defined by your team, the way building delivers. You are not choosing between fit and reliability. You get both, because the heavy, undifferentiated work is carried by the platform while the part that actually reflects your edge stays in your hands.

This is what the Orbit Agent Builder is. An investment team describes its method in plain language, or uploads a document that already captures it, and the platform turns that into a production-grade agent running on proven infrastructure. The data layer, the document processing, the scheduling, the monitoring, and the governance come with the platform. The method, the logic the agent actually follows, is yours. You own the part that makes you distinctive. You do not own the upkeep that makes you slow. And because building an agent no longer requires an engineering project, the people who understand the method best are the ones who put it to work, without a queue or a translation layer in between.

This also changes the economics of experimentation. When building an agent is a months-long project, every agent has to justify itself in advance, and most good ideas never clear that bar. When building an agent takes a single session, the cost of trying something drops to almost nothing. Teams can test a research idea, see whether it holds, refine it or discard it, and move on. The method improves because improving it is finally cheap. That is the part the old debate misses entirely. Build versus buy is an argument about how to acquire a fixed capability. The more interesting question is how quickly your capability can keep changing.

So the right question is not build or buy. It is whether you can get the fit of a build with the speed and reliability of a buy, and keep the freedom to refine your method as fast as your thinking evolves. When that becomes possible, the old debate simply dissolves, and the teams still arguing it are spending energy on a choice they no longer have to make.