The future is products that grow themselves!#
@Scale: Product 2026 digest Program Core pick 28 October 2026
Jubin Chheda (Meta)
- Why: The closest thing at this event to agents operating on infrastructure economics: an autonomous system that finds and ships compute-efficiency wins, with quantified production results. Directly relevant to agentic systems applied to operations and cost.
- What: Wally, a self-learning loop of agents at Meta — an optimizer, verifier, and learner — that identifies opportunities to save CPU or GPU cycles, verifies the change for correctness and safety, runs a public A/B test, and launches the winners. Claimed results: thousands of public tests, a 30% success rate, and several million dollars saved. (from abstract only)
- How: A closed loop: the optimizer proposes changes, the verifier gates them on correctness/safety, a live A/B test validates them on real traffic, and the learner updates the harness based on wins and fails — so the loop itself improves over iterations.
- Where applicable: Meta-scale production with a mature experimentation platform. The loop architecture (propose → verify → A/B → learn) is general, but the 30% success rate and dollar savings depend on Meta's traffic volume and A/B infrastructure. It assumes a public-facing product with enough users for statistically meaningful experiments — not directly portable to internal-only enterprise environments.