AnswersPrivate equity
In short
Private equity firms can deploy AI through lab-owned deployment firms, consultancies, integrators, platform partners or their own teams. Four questions separate them: whose models the work runs on and whether that can change, who owns what gets built, who runs it after the engineers leave, and how the partner's incentives line up with the value creation plan.
| Question | Why it counts over a hold |
|---|---|
| Whose models, and can that change? | Model leaders change every few months, and a hold lasts years. A partner tied to one lab ties the plan to it. |
| Who owns what gets built? | The context, evaluations and trained models should stay with each portfolio company. |
| Who runs it afterward? | Systems need tuning after launch, and the company needs a platform and a team to run them. |
| How do incentives line up? | Fees should follow measured results, and a partner should not be paid by the supplier it recommends. |
Lab-owned deployment firms build on their parent lab's models. Consultancies deliver strategy and programs on platforms licensed from the labs. Integrators run large projects and keep the capability in their people. Platform partners bring their own platform and engineers. In-house teams own everything and build it from the start.
Fig is a platform partner: CosmOS across every frontier lab, engineers inside each company, a pilot in production in weeks, and modules reused across the portfolio. Each company keeps what is built, and every system reports against its baseline.
What should PE operating partners look for in an AI platform? →
A small firm-level team helps set priorities and share what works. The platform and the engineers inside each company are usually better sourced from a partner.
Yes, if its platform is industry-agnostic and lets each company keep its own data while modules move between them.
By measured results in each company against a baseline set before the build.
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