Key Takeaways

  • AI is melting traditional product development roles. Akshay Nathan from OpenAI sees the lines blurring between product managers, engineers, and designers as AI tools let individuals pursue what he calls "bottoms-up ambition."
  • Forget pure specialists. The future professional is a "T-shaped generalist" – deep expertise in one area, complemented by AI-enabled breadth across many others. As Nathan puts it, "AI will enable everyone to become a generalist... But then people will have a specialty."
  • Old productivity metrics like code commits or story points are obsolete. Nathan states they are “starting to fall apart” because AI can generate vast amounts of "motion" without driving real progress.
  • New success measures center on "at-bats": how efficiently a team iterates from an initial idea to a validated or invalidated hypothesis. It's about quality and speed of learning, not just raw output.
  • The "Motion vs. Progress" Rule helps teams distinguish busywork from actual impact when AI makes generating "motion" incredibly easy. It forces a deliberate focus on measurable outcomes.

The "Motion vs. Progress" Rule for AI-Enabled Teams

The Problem: conflating motion and progress. I think motion is much easier now than ever before because of the tooling that we have.

The Solution: progress requires you to be like very prescriptive and deliberate about like what you're actually trying to achieve. And it goes back to our question of measurement... as a team like you should have a really prescriptive and deliberate view on like what progress looks like for you and for your team.

Measuring Progress ("At-Bats"): Are we as a team building the muscle to have not just quantity of at-bats, but quality? Like, are we able to go all the way from like generating an idea, building it out, getting the feedback, reacting to that feedback, actually validating or invalidating the hypothesis, going on to the next idea? Are we able to do that really efficiently?

When This Works (and When It Doesn't)

Akshay Nathan suggests this framework is for teams “on the frontier of this technology” working directly with AI tools. It shines when AI dramatically lowers the cost and time of creating output – like code, content, or designs. In these scenarios, the danger isn't a lack of production, but a flood of "motion" that doesn't solve real problems. The true bottleneck shifts from execution to good ideas, taste, and rapid validation. As Nathan says, "I think the bottleneck some becomes like sort of like ideas and taste I guess. Um I think because anyone can can build now, I think um it really is the era of like bottoms-up ambition."

This rule is less critical for teams whose core work isn't heavily AI-assisted, or where human creativity and judgment are still the primary drivers of the initial "motion" itself. It implicitly assumes a high-trust environment where team members can experiment and fail fast, rather than being judged on activity alone. If your team lacks autonomy or clear strategic direction, simply tracking "at-bats" without proper guardrails could lead to a different kind of undirected motion.

What to Do With This

Imagine your team is pumping out AI-generated features at an incredible pace, but your core user metrics aren't budging. You're deep in "motion," not "progress." Tomorrow, gather your team and apply the "Motion vs. Progress" Rule. First, define your team's single most critical "progress" metric for the next 90 days – maybe it's a 15% increase in weekly active users, or a 10% reduction in customer churn for a specific segment. Then, for "Measuring Progress ('At-Bats')," decide what an "at-bat" looks like for your specific goal. For instance, an "at-bat" might be: "Identify a user pain point -> develop an AI-powered solution hypothesis -> build a minimal version -> get user feedback -> validate or invalidate the hypothesis within 72 hours." Implement a weekly "at-bat review" where you assess not how many features shipped, but how many hypotheses were tested, what was learned from each cycle, and how efficiently your team moved through that full validation loop.