Key Takeaways

  • AI tools like Cloud Code and Codeax aren't just incremental improvements; they dramatically accelerate engineering output, letting teams build complex products in weeks, not months.
  • FOMO built a complete trading web app in just one month and another core product in three weeks, a pace Erlang attributes directly to AI-enhanced workflows.
  • The speed of AI development means engineering productivity shifts from code writing to review and restructuring, even for senior staff like his engineer Tina who can rewrite most generated code faster.
  • Erlang predicts companies will soon spend 20% of developer salaries on AI tokens, calling it a necessary investment to unlock this new level of velocity.
  • This AI-driven productivity makes smaller, high-equity engineering teams viable and powerful, challenging the traditional skepticism from larger tech companies.

AI: The Engineering Force Multiplier

Forget the generic hype about AI boosting productivity. Paul Erlang, co-founder and CEO of FOMO, offers concrete, almost alarming, specifics. His team isn't just seeing marginal gains; they're fundamentally re-architecting how quickly software gets built. Erlang points to tools like Cloud Code and Codeax not as assistants, but as core accelerators that shrink timelines from months to weeks.

Consider FOMO's own track record: they built a complex trading web application in just one month. Another product, which Erlang says represents the entire offering of some competitors, was shipped in a mere three weeks. “We built this product in three weeks and this product is basically what entire other apps their entire product is. You know, we built our web app in one month,” Erlang stated, underscoring the raw speed.

The real shift isn't just about cranking out more lines of code. It's about how quickly that code can be reviewed, refined, and learned from. Erlang notes that even his top engineers, like Tina, leverage AI to quickly generate frameworks. “Tina will go back through and even restructure and rewrite most of the code. But having the framework of understanding how to write it, I think just speeds up the learning process significantly even for the best engineers,” he explained. This isn't about replacing engineers; it's about making them far more effective, turning every senior dev into a super-architect capable of overseeing massive codebases generated at an unprecedented pace.

Smaller Teams, Bigger Equity, Outsized Impact

This explosion in engineering output has profound implications for team structure and incentives. Erlang's core thesis is simple: if each engineer can now achieve 5x or 10x the output, why maintain sprawling, traditional teams? AI makes a lean, high-performing squad not just efficient but dominant. This informs FOMO's unconventional approach to granting significant equity early, even to non-founders, because each 'architect' engineer becomes exponentially more valuable.

Erlang doesn't shy away from challenging the skepticism often voiced by leaders at giants like Uber and Microsoft. He believes these larger companies are missing the point, fixated on old paradigms of productivity. For FOMO, the investment in AI isn't an option; it's a strategic imperative. “Can you feasibly see yourself spending 20% of dev salaries on tokens? Definitely. Yeah,” Erlang affirmed. This isn't a small line item; it's a reallocation of core resources, recognizing that the computational power behind AI is the new raw material for software development.

This shift means the 'best people' are no longer just good at writing code; they're adept at directing AI, shaping its output, and applying creative direction. “The best people are just so much more valuable now because it's like you can use you can use chat GPT to make art, but you need to have like the creative direction behind it,” Erlang said. This isn't just about being a coder; it's about being a conductor of AI, leading to unparalleled product velocity with far fewer hands.

What to Do With This

This week, audit your current engineering budget. Specifically, identify 5-10% of your current or projected developer salaries and allocate it to an experimental AI token budget for your engineering leads. Task them with identifying one new, complex feature or component they can build in half the expected time using AI tools like Cloud Code or Codeax, reporting back on specific time savings and the quality of the AI-generated code.