When Claude Starts Driving Its Own Growth, What Does Anthropic Really Care About?

Anthropic is at the center of AI industry attention as its models become markedly more capable while product lines expand quickly, creating pressure to maintain orderly growth, lower product barriers, redesign experimentation, and embed safety considerations into its growth logic.

Recent events illustrate the stakes: on March 5 the U.S. Department of Defense listed Anthropic as a supply‑chain risk (temporarily halted by a federal judge on March 26); on March 31 a human error leaked roughly 500,000 lines of Claude Code, prompting scrutiny of internal security; and on April 7 the limited‑access Claude Mythos Preview was released alongside Project Glasswing after high‑severity vulnerabilities were discovered in mainstream OSes and software.

In a Lenny’s Podcast interview, growth lead Amol Avasare explained that, beyond model performance, the team now focuses on how users perceive and initially engage with the product, noting that stronger models paradoxically make the product harder for users to adopt.

Avasare highlighted the prevalence of “Success Disasters” – chain reactions of new problems that emerge during explosive growth. He estimates about 70 % of the team’s effort is spent firefighting these issues, which arise from rapid expansion across acquisition, onboarding, monetization, pricing, and product packaging.

The most difficult problem, according to Avasare, is the “initial experience” after a user enters the system. The speed of model iteration forces product teams to translate technical advances into intuitive, visible, and useful interaction points, a conversion process that grows exponentially more complex compared with traditional software development.

Even though model capabilities have leapt forward, ordinary users often lack awareness of new features and become unsure where to start. To address this, Anthropic is developing a memory function for its ChatGPT‑like product to bridge cold‑start gaps, allowing Claude to quickly recognize a user’s identity and needs and steer them toward the most effective features.

The growth team also emphasizes early user profiling to dynamically match product functions with the right audience. Avasare recalled a quarter‑long effort at his former company Mercury to overhaul the guidance flow, which later boosted end‑to‑end conversion rates. Anthropic applies the same methodology: the system asks users about their identity and core concerns early on, then tailors feature recommendations accordingly.

Adding interaction steps is not seen as a barrier; rather, necessary steps that help users build mental models of the product and understand its value actually clear cognitive blind spots and improve overall efficiency in reaching core use cases.

Looking forward, Avasare says the growth function is shifting toward “big moves” with strategic significance: scaling product actions, automating experiment pipelines, and reshaping collaboration among product managers, engineers, and designers to support AI‑native product growth beyond incremental conversion tweaks.