#ai-architect

#ai-architect

Earlier we did episode one of this with Grady Booch where we discussed the principled view of that what's changing and what remains unchanged, what is hyped and what is actually naturally coming with the AI changes. We also spoke about that what is the difference between the design and the architecture and what teams are focusing and what they might be missing.

The model's other capabilities, including support for multimodal inputs, multiple reasoning modes, and parallel sub-agents for complex queries, could help enterprises build faster, task-focused AI for customer support, automation, and internal copilots without relying on heavier models.

Baron traces the origin story back to his time building high-scale systems at Instana (which exited to IBM in 2020), where the reality of "always-on" platforms made one thing obvious: the tooling we rely on is often too low-level, too rigid, and too disconnected from real-world use cases. That gap has only widened as environments have exploded in complexity-more cloud providers, more managed services, more hybrid setups, more internal APIs, and "gillions" of tools stitched together into brittle workflows.

Between the lines: This isn't benevolence. It's customer acquisition. Mayors don't just buy "AI." They buy cloud, data modernization, cybersecurity, services, and long-term support - the tech stack underneath any serious deployment. In return, cities get tools that could fix long-standing challenges, Cris Turner, vice president of government affairs at Google told Axios last June when it first released its playbook.