A key expansion strategy is moving 'upper funnel' from treating specific, acute conditions to offering a holistic, preventative platform. For Hims & Hers, adding diagnostics ('Labs') created a new entry point for users to understand their overall health, not just solve one problem.

Many pharma companies have breakthrough AI results in isolated functions, or "pockets of excellence." However, the ultimate competitive advantage will go to the company that first connects these disparate successes into a single, integrated, enterprise-wide AI capability, thereby creating compounded value across the organization.

While digital advertising constitutes 75% of spend in the general economy, it's only about half that in healthcare. This lag, driven by an entrenched reliance on in-person sales reps, creates a long-term secular tailwind for platforms like Doximity as the industry inevitably shifts its marketing budget online.

Unlike incumbents, new biotech and pharma companies often lack established sales forces. They launch with a 'digital first' go-to-market strategy, turning to platforms like Doximity early in their lifecycle. This creates a new and rapidly growing customer segment for Doximity, independent of the incumbents' slower transition.

Doximity integrates multiple workflow tools like telehealth and e-signatures. While specialized competitors might offer better individual products, Doximity wins by providing a convenient, all-in-one platform that doctors are already engaged with daily, creating a powerful defensive moat.

OpenAI's launch of ChatGPT Health, which integrates medical records, signals a clear strategy to move beyond general-purpose APIs. Foundation model companies are now building specialized, vertical-specific products, posing a direct threat to "wrapper" startups that rely on the underlying models' existing capabilities.

Instead of competing on diagnostics, Anthropic is positioning its Claude model as an 'orchestrator' to unify disparate health data for patients and providers. This strategy targets a major pain point—system navigation and data integration—rather than directly challenging established medical AI use cases, carving out a unique enterprise niche.

Point-solution SaaS products are at a massive disadvantage in the age of AI because they lack the broad, integrated dataset needed to power effective features. Bundled platforms that 'own the mine' of data are best positioned to win, as AI can perform magic when it has access to a rich, semantic data layer.

The primary challenge for direct-to-consumer (DTC) AI doctor services is not technology but economics. High customer acquisition costs and churn make a standalone subscription model untenable. Successful AI doctors will likely be a top-of-funnel feature for a larger, integrated healthcare business.