Responsive Software
Software has changed a lot since its infancy in the 1950s. However – from early mainframes and monolithic architectures to modern unstructured databases and distributed architectures – the fundamental pattern has remained the same: moving data to a central location for processing. The technology has changed from mainframes to SQL databases to cloud data warehouses & lakehouses, but the core pattern of data centralization persists.
This doesn't make sense anymore. The volume of data organizations need to store and process is ever-increasing, and importantly, we've reached the point where real-time needs begin to balloon. Companies on the bleeding edge of technology (e.g. TikTok's real-time recommendations, Stripe's fraud detection models) also recognize this.
AI in particular demands real-time data. While pre-training spurred initial improvements with LLMs, post-training reinforcement (i.e. like DeepSeek-R1) will power further advancements. Today, brittle AI agents struggle with fluid workflows that demand constant adaptation. In order for them to learn on the fly, AI agents need to process & act on new information in real-time.
The current improvement loop for AI, with human intervention, is manual and time-consuming. What's become abundantly clear to us is that human data should no longer be the gold standard now that models are able to self-improve. A more elegant approach would be to merge inference costs with training costs, and simply train AI on its own outputs to move towards real-time responsiveness.
Right now is an especially exciting time to be working on software, and we think what's being done with real-time data (across consumer, fintech, AI, and more) is just the beginning.
We're captivated by the concept of a future full of responsive software. Where dynamic traffic lights and routing alleviate city congestion. Where daily chores can be automated without manual prompting. Where proactive detection of health concerns can save lives.
We want to make that future a reality.