====== Today in AI: May 09, 2026 · 4 min read ======
**Superhuman hit 200K requests per second—and load balancing became the bottleneck.**

[[power_of_two_choices|Power-of-Two Choices]] isn't fancy, but it works. Instead of round-robin, you sample two nodes per request and pick the one with lower load. Superhuman and [[https://www.databricks.com/blog/how-superhuman-and-databricks-built-200k-qps-inference-platform-together|Databricks built a 200K QPS inference platform]] using this technique, handling grammar correction at scale for [[coda|Coda]]. At that throughput, traditional routing explodes. Infrastructure gets unsexy when it actually has to work.

🚀 **Databricks shipped the MCP Marketplace, turning agent data access into plug-and-play.**

[[mcp_marketplace|MCP Marketplace]] is a governance layer for Model Context Protocol servers—think AppStore for agentic data sources. Agents can now discover and integrate external data sources with built-in control mechanisms. This matters because agents without reliable, governed access to real-time data are just expensive chatbots. Databricks is betting that enterprise AI runs on composable infrastructure, not monolithic models.

🏗️ **Voice agents are eating the middle of the call center stack.**

[[autonomous_tool_control|Autonomous Tool Control]] capability means agents can now operate software interfaces, not just generate text. Combined with [[https://www.theneurondaily.com/p/openai-s-gpt-realtime-2-is-coming-for-call-center|voice-based reasoning and configurable inference budgets]], this unlocks real customer service automation. [[attio|Attio]] (AI CRM) and [[zillow|Zillow]] voice agents show the pattern: if you can coordinate tools + voice + reasoning, you don't need humans for tier-1 support. The economic pressure is immediate.

💰 **Frontier labs are hiring; everyone else is pretending to do AI.**

[[frontier_companies_vs_layoff_companies|Anthropic is growing 10x year-over-year while traditional tech announces layoffs.]] The divergence is stark. Real AI builders are scaling fast. Everyone else is doing "AI-washing"—sprinkle Claude or GPT into a product, announce it as transformation, then cut costs. The industry's doing natural selection in real-time.

🔬 **Open-weight models have already won machine translation.**

[[proprietary_vs_open_weight_translation|Proprietary translation (DeepL) vs open-weight models]] is no longer a fair fight. Open models are cheaper, faster to deploy, and good enough. The economic moat evaporated. This pattern repeats: whenever a task becomes commodified, open weight wins. Builders should stop licensing proprietary APIs for solved problems.

🎯 **Still no Claude Mythos broad release, GPT-5.5 is live but [[gpt_realtime_2_vs_gpt_realtime_1_5|real-time reasoning is the real story]], and [[zyphra_ai|Zyphra is quietly shipping open-source MoE models.]]**

That's the brief. Full pages linked above. See you tomorrow.