DigitalOcean AI newsletter

ICYMI AI: The latest AI trends, delivered to your inbox Tech talk: The AI stack is more modular and multi-modal than ever With a changing landscape of models, standards, and systems, no two AI tech stacks are alike. This open ecosystem is the emerging standard of architecture, something last week’s Open Intelligence Summit (OIS) highlighted with speakers from NVIDIA, Amplitude, Open Hands, and Nous Research. According to internal DigitalOcean figures, 88% of our tokens run on open-weight models, a 27% increase from May 2026. This is accompanied by a 50% growth in token volume over the last six months. It’s clear that loyalty to a single model is fading, and a new layer is emerging to manage churn: routers. Analysis from Vals AI frames routers as a new AI primitive. They choose models based on quality and cost per request, so applications don’t need to revisit model selection every time something new launches. They also provide more opportunities for developers to select models based on use case, rather than being locked into frontier models or a specific provider. Open standards are doing the work that closed-loop systems and frontier models used to do, and the tooling is adapting: DeepSeek opened Search and Fetch APIs that return live web content as Markdown for agents and RAG pipelines. Perplexity open-sourced its agent sandbox. Anthropic’s OpenTelemetry support means traces from different providers can share the same dashboards. When models are interchangeable, differentiation moves to the layers you control: evaluation, observability, security, and cost. With the ability to fine-tune multiple layers, developers need concrete plans for AI stack management. Abstract your model calls behind a routing layer. Standardize on OpenTelemetry for agent traces. Build evals you can rerun against any model. And keep a cost dashboard open. Our new Managed Agents and Agent Learning Services, announced at OIS, paired with our cache-aware Inference Router, allow you to build AI products and own your intelligence. Industry news AI adoption looks broad until you check what’s measurably working. a16z’s State of Markets II presentation found that 69% of S&P 500 companies have a live AI deployment and about 30% report a quantified result, but only around 2% track a metric for it, while the top 1% of companies by AI spend outspend the top 10% roughly eightfold. The gap is less about access to tools than about tying AI to a workflow you can measure. ChatGPT is shifting from walls of text to answers you can interact with. OpenAI’s GPT-6 launch introduced “Intelligent UI,” which lets the model respond with charts, buttons, forms, and quick tools like savings calculators or bill splitters, all inside the conversation. OpenAI also says GPT-6 Instant starts answering web-search questions 44% sooner on average than GPT-5.6 Instant. Community tutorials Agents in Production, Part 1: Build an Overnight AI Support Agent using Managed Agents An unattended support agent ran for 1 hour 48 minutes against a live board, triaged 99 tickets, stopped all 35 sensitive ones for a person, and sent zero replies, because the agent was never given a route to send. Learn how to scope board credentials, restrict egress to a single host, and add deterministic checks to drafts so the agent can’t act outside its brief. How to Safely Let AI Agents Call Production APIs using DigitalOcean Action Gateway Across 1,300 episodes, 45 to 48 of every 100 runs without a gateway ended with at least one harmful change, and the agent re-sent a refund in 66 of 75 tries. See how idempotency keys, version checks, and approval tiers brought harmful changes to 0 of 100. What real time-to-first-token looks like on DigitalOcean Serverless Inference Across 380 calls to six models, five landed between 0.88 and 0.98 seconds median TTFT on short prompts, but DeepSeek V3.2 hit 78.3 and 186.9 seconds on two of 80 calls. Get the measurement method and code repository to run the sa