Trading Margin for Moat: Why the FDE Is the Hottest Job in Startups
It turns out dethroning Salesforce isn’t as simple as spinning up an OpenAI-enabled voice agent. Companies attempting to replace core workflows owned by legacy systems of record with lightweight, wrapper-like integrations too often see their agents break or fail outright.
Paradoxically, we’re witnessing explosive growth for complicated AI products in sales, support, and legal. Enterprise software companies that tackle complex workflows are regularly growing from $0 to $5 million, $10 million, or beyond $20 million in ARR in their first two years. So how are new AI startups solving for these intricate use cases?
The implementation necessity
Category-defining companies like Salesforce, ServiceNow, and Workday were able to achieve success largely because they did a significant amount of implementation work. They became indispensable by integrating with companies' internal systems and context and becoming their clients' source of truth. Enterprise AI products have an even more pronounced implementation requirement, as they require deep integrations and context. If emerging AI applications really want to succeed, they should position themselves to become the system(s) of work that generate, capture, and store valuable company data.
That's where the forward deployed engineer comes in.
As the tasks AI is called upon to execute grow more complex, fulfilling those requirements becomes increasingly challenging. Companies will need expert services to redesign job functions and processes around this AI-first approach. Without hands-on implementation support, AI risks falling short of the standards set by a dedicated employee. With the right support, however, agents can unlock far greater business value than basic task automation ever could.
In the process, as AI startups race to own the data ingestion layer, I'd argue it’s shortsighted to be optimizing for 80% gross margin. Founders who obsess over gross margin and scalability, while neglecting the hard problem-solving required to own the system of work, risk missing the forest for the trees. We believe the companies that will dominate this platform shift will be the ones that effectively replace human workflows with AI, capture critical company data, and solve genuine business problems — even if that means digging in with implementation work along the way.
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Honestly, this hits home for me. I've seen firsthand how crucial human touch remains, even as AI evolves.
Great read that highlights an insightful and often overlooked truth: in the AI era, implementation is innovation. The idea that forward-deployed engineers are the connective tissue between foundational models and practical applications is spot-on, and it’s encouraging to see services reframed not as a drag on margins but as a strategic moat. However, here’s where I hold a slightly different view: as AI takes on more decision-making responsibility, human empathy and trust become even more vital, not less. CX isn’t just about speed or automation; it’s about emotional trust, empathy, and reliability.
Insightful 💡
Another variation of FDE = Forward Deployed Executive = Field CTO