In the past year, more and more AI startups have been heard saying during fundraising roadshows: "We are the Palantir of the X field." These companies follow a similar playbook—deploy engineering teams directly into the client organization, deeply customize business processes, and claim to quickly deliver usable systems in complex enterprise environments. By 2025, the demand for frontline deployment engineers (FDEs) has multiplied several times, indicating that this model is being rapidly copied.

But this approach might not be as attractive as it seems. Marc Andrusko, a partner at a16z focusing on AI application investments, poured some cold water on it, saying that this "Palantirization" trend is more of a high-risk shortcut than a universal solution for most startups.

**Why is everyone trying to emulate Palantir?**

After AI enters the enterprise implementation phase, the real challenges begin to surface. One major issue is that enterprise AI projects often get stuck and never go live. Data is scattered everywhere, legacy systems pile up, internal responsibilities are chaotic, leading many AI procurement projects to remain in the proof-of-concept (PoC) stage forever. Boards and executives are eager to buy AI, but cases that actually run in production are few and far between.

Another factor is that FDEs have become key to bridging this gap. By embedding engineers directly into the client organization, theoretically, they can better understand business logic, connect various systems, and deliver tangible results. This has become a winning strategy for startups competing for seven-figure contracts.

There’s also a practical consideration: high-value contracts are easier to generate impressive growth curves than the PLG (product-led growth) model. In the current funding environment, securing contracts worth millions annually with relatively low gross margins makes for a more compelling story to investors.