LazyAI

Large parameter counts are no longer the only metric that matters. Several engineering teams released efficient transformer variants and training recipes that reduce compute needs by 5–10x for comparable task performance. For product teams this means rapid iteration: you can run helpful models in the background of mobile apps, deliver voice assistants with low latency, and significantly reduce hosting costs.

Practical takeaway: prioritise latency and safety usage budgets when choosing a model for production. Benchmark on device and include a fallback to a server-side model for rare heavy tasks.

Teams should implement minimal compliance foundations early: clear model cards, access logging, and a simple incident playbook. These practices reduce legal exposure, help with customer trust, and make debugging simpler. Treat them like reliability engineering: the earlier, the cheaper.

Practical takeaway: add a lightweight "safety & logs" tab to your admin dashboard that stores model metadata, recent prompts, and anonymised failure cases.

Successful pilots explicitly measure clinician time per task and include UX changes — not just model improvements. The difference between a useful tool and a shelf project is often integration: EHR adapters, one-click templates, and clear audit trails.

Practical takeaway: ship an integration adapter and a short training session. If clinicians see immediate time saved, adoption follows.

Investors look for revenue proxies. If your demo is 80% reliant on bespoke data cleaning or expensive human-in-the-loop fixes, build product affordances that hide complexity and surface the value. Example: a single-button "Summarise & send" feature that replaces a slow multi-step workflow.