Meet the former Apple designer building a new AI interface at Hark

The integration of hardware, models, and interfaces signals a fundamental shift in how AI products will be consumed—and this directly impacts our data pipelines. When design teams have hardware constraints baked into the architecture from day one, it changes everything about latency requirements, batch vs. streaming decisions, and edge processing strategies. We're likely looking at products that demand sub-100ms response times, which means traditional data warehouse architectures won't cut it. Data engineers building for these next-generation interfaces need to think in terms of real-time feature serving, not nightly transformations. This also suggests the platforms we adopt will increasingly bundle inference with data infrastructure—think Databricks or similar players gaining deeper embedded analytics. My recommendation: audit your current data stack's latency profile. If you're optimizing for hourly SLAs, you're already behind. Start experimenting with feature stores and streaming architectures now, because the design-first companies will demand this from their infrastructure partners within 18 months.