AI's Human Touch: Why Forward Deployed Engineers Aren't Going Anywhere

AI was supposed to be as easy as turning on a faucet. But reality checks in when companies like OpenAI and Anthropic start embedding engineers within their clients’ teams. Why the hands-on approach for something that’s supposed to be a utility? Let's break it down.

The Rise of Embedded Engineers

In a surprising twist, leading AI firms are sending tech experts directly into organizations. OpenAI announced a specialized unit to incorporate 'Forward Deployed Engineers' into workplaces dealing with complex issues. These experts work alongside teams to figure out where AI can really make a difference. Not just that, they're redesigning workflows to ensure long-term benefits.

Anthropic and Google have also jumped on this bandwagon. They're embedding engineers with clients to push AI adoption and develop real-world applications. It’s like an elite squad swooping in to save the day, but why is it necessary?

Here's the kicker. If AI were truly a utility akin to electricity or water, we wouldn’t need to send in engineers to make possible it. The promise was of an abundant resource available on demand, not a consulting gig. Yet, here we're, with engineers becoming an essential part of the AI puzzle.

Why AI Isn't Plug-and-Play Yet

This hands-on approach reveals a crack in the AI promise. Companies are realizing that deploying AI is more like bespoke tailoring than off-the-rack shopping. Forward Deployed Engineers tackle the nitty-gritty, permissions, data quality, and system constraints. They’re basically the bridge between new AI models and messy organizational realities.

But that’s precisely the issue. If AI were truly scalable, like mature cloud services, companies wouldn’t need to rely on such human intervention. Think of it this way: SAP and Salesforce don't send employees to every client. They tap into a partner network. So what's stopping AI from doing the same?

Right now, Forward Deployed Engineers are a sign of AI's growing pains. They're essential in making AI work for real-world businesses, but they also signal that AI isn't quite ready to stand on its own yet. In this pre-platform phase, AI's potential is bottled up, waiting for that scalable magic to happen.

The Future: From Artisans to Platforms

We’re in the 'artisanal' phase of AI deployment. Before cloud platforms became plug-and-play, they too required a small army of specialists. History shows us that every technology starts like this before it finds its platform footing.

What’s next? For AI to really take off, it'll need to transition to a repeatable, scalable model. That would mean less reliance on embedded engineers and more on partners and templates. A true AI platform would let companies deploy AI without needing a full-time hand-holder.

But here’s a thought: Can AI companies shift gears when their current model is a lucrative business? The innovator's dilemma kicks in. Forward Deployed Engineering is a moneymaker, and moving away could be financially risky. Yet, the real leap forward will come from building a layer that replaces these bespoke solutions with a universal one.

The moment that happens, the current model will seem outdated. The industry will pivot fast, just like when cloud services matured. Technology doesn’t scale by sending engineers to every customer. It scales when the infrastructure’s already there.