u/Such-Run-4412

Anthropic is reportedly in talks to rent servers powered by Microsoft’s own AI chips, giving Claude another possible source of compute as demand for AI services keeps rising.

The talks are still early and may not lead to a final deal. But if it happens, it would be a major win for Microsoft’s custom chip ambitions.

The chip in question is Maia 200, Microsoft’s second-generation in-house AI accelerator. Microsoft built it for large-scale AI inference, meaning the expensive everyday work of running AI models after they are trained — answering prompts, powering assistants, and serving millions of users.

This matters because the AI race is no longer just about who has the smartest model. It is also about who can secure enough compute, lower inference costs, and avoid being fully dependent on Nvidia GPUs.

Anthropic already works with Amazon and Google for AI infrastructure, so adding Microsoft chips would make its compute strategy even more diversified. That is important because Claude’s usage is growing, and frontier AI companies cannot afford to rely on one cloud provider or one chip supplier.

For Microsoft, this would be a big credibility boost. Google has TPUs. Amazon has Trainium. Microsoft wants Maia to become a serious part of the AI cloud stack too.

The bigger takeaway: AI labs are turning into infrastructure strategists. The companies that win may not just be the ones with the best models, but the ones that can get enough chips, power, and data center capacity to actually serve those models at scale.

Source: https://www.bloomberg.com/news/articles/2026-05-21/anthropic-in-talks-to-use-microsoft-ai-chips-information-says

Google’s latest AI push looks less like a normal model update and more like a full ecosystem play.

The headline model is Gemini 3.5 Flash, which is becoming the default model for the Gemini app and AI Mode in Search. It is built for faster output, better coding, stronger agentic task handling, improved multimodal work, richer interactive UI generation, and better safety guardrails.

But the bigger Gemini 3.5 Pro model is not out yet. Google says it is still being tested and is expected next month.

The most interesting part is how aggressively Google is pushing agents everywhere.

Gemini Spark is Google’s new personal AI agent that can work across Gmail, Docs, Sheets, Slides, and other Google apps. It can run in the background even when your phone is off or laptop is closed, because it runs on Google’s virtual machines. The goal is basically a 24/7 personal assistant that can track tasks, handle recurring workflows, and act across your digital life.

Google is also adding a Daily Brief, which sounds like a lighter version of Spark. It looks at your email, calendar, tasks, and other personal context, then prioritizes what matters and suggests next steps.

Search is changing too. AI Mode now runs on Gemini 3.5 Flash and supports text, images, files, videos, and even full Chrome tabs. The big shift is that Google Search is moving away from just “ten blue links” and toward interactive answers, generated tools, agents, and ongoing task completion.

That has huge implications for publishers. More answers inside Google means more zero-click searches, where users get what they need without visiting the websites that created the original content. This could be great for users, but rough for websites that depend on Google traffic.

Google is also pushing agentic shopping through something called a universal cart. The idea is a Gemini-powered shopping layer that works across Search, Gemini, YouTube, Gmail, and major retailers like Nike, Target, Walmart, Sephora, Wayfair, and Shopify. It can track prices, find coupons, alert you when items are back in stock, and possibly warn you if something is incompatible with your setup.

Then there are the AI glasses. Google is working with Samsung, Qualcomm, Gentle Monster, Warby Parker, and others on Android XR eyewear. The first version seems focused on audio, Gemini voice assistance, live translation, navigation, and hands-free interaction. Display glasses are still coming later.

Google is also turning more of its apps into AI surfaces. Gmail gets AI Inbox and Gmail Live. Docs gets voice-powered editing through Docs Live. Keep gets Keep Live for capturing messy thoughts and turning them into organized notes. YouTube gets Ask YouTube, which lets users ask questions about videos and jump to relevant timestamps.

The infrastructure side may be just as important. Google is partnering with Blackstone on a TPU-powered AI cloud venture, with Blackstone reportedly putting in $5 billion in equity. The goal is to expand compute capacity as AI agents become more common and more expensive to run.

Video URL: https://youtu.be/cSZ-y49e5q8?si=reUrkXPqT4H-9rgj

Nous Research’s Hermes Agent now has a full skills system, and the most interesting part is skill bundles.

Skills are reusable instruction files that an agent can load only when needed. Instead of stuffing every workflow into the system prompt, Hermes keeps skills separate and pulls them in on demand. That makes the agent more token-efficient, meaning it wastes less context space on instructions it does not need.

Each skill works like a slash command. For example, you can call a coding workflow, a planning workflow, or a GitHub PR workflow directly inside Hermes. The agent can also discover skills naturally during chat.

The bigger update is skill bundles. These are small YAML files that group several skills together under one slash command. So instead of loading three separate skills one by one, you can create something like /backend-dev and have it load code review, test-driven development, and PR workflow instructions all at once.

That matters because real agent work usually needs more than one capability. A backend task might need planning, testing, code review, and GitHub steps. An incident response task might need logs, debugging, deployment, and documentation. Skill bundles turn those repeated combinations into one reusable workflow.

Hermes also supports external skill directories, meaning teams can share skills across machines or repos. It can install skills from hubs and registries, and agents can even create or update their own skills after learning a useful workflow.

The big idea here is procedural memory for agents. Instead of treating every task like it is brand new, Hermes can save the steps that worked and reuse them later.

This feels like another sign that AI agents are moving away from simple chatbots and toward configurable work systems. The next wave may not just be “smarter models.” It may be agents with reusable playbooks, team workflows, and shared skill libraries.

Source: https://hermes-agent.nousresearch.com/docs/user-guide/features/skills#skill-bundles

Anthropic has agreed to pay SpaceX nearly $45 billion over the next three years for AI computing resources, making this one of the biggest AI infrastructure deals we’ve seen so far.

The deal works out to about $1.25 billion per month, or roughly $15 billion per year, running through May 2029. The compute will support Anthropic’s AI products, especially Claude, as demand for inference keeps exploding.

The interesting part is that this pushes SpaceX deeper into the AI infrastructure business. SpaceX is not just launching rockets anymore. It is becoming a major compute provider through its large AI data centers, reportedly including Colossus and Colossus II in Tennessee.

There are some flexible terms. Either Anthropic or SpaceX can walk away with 90 days’ notice, and the fees are lower during the early ramp-up period while capacity comes online.

This also shows how expensive the AI race has become. Even a top AI lab like Anthropic now needs massive outside infrastructure deals just to keep up with usage. The bottleneck is no longer only model quality. It is power, GPUs, data centers, and who can secure enough compute.

For SpaceX, this could become a huge new revenue stream before its IPO. For Anthropic, it gives Claude more compute at a time when the company is reportedly growing fast and moving closer to profitability.

The big takeaway: AI companies are turning into infrastructure companies by necessity, and infrastructure companies are turning into AI companies by opportunity.

Source: https://www.bloomberg.com/news/articles/2026-05-20/anthropic-to-pay-spacex-nearly-45-billion-for-computing-deal

Google just introduced Gemini 3.5, starting with Gemini 3.5 Flash, and the main pitch is clear: this model is built for action, not just chat.

The big upgrade is agentic work. Gemini 3.5 Flash is designed to plan, use tools, write code, manage long tasks, and run multi-step workflows with less waiting. Google says it is its strongest model yet for agents and coding, while still keeping the speed people expect from the Flash line.

It is already available in the Gemini app, AI Mode in Google Search, Google Antigravity, Gemini API in AI Studio, Android Studio, and Google’s enterprise AI platforms. That means this is not just a lab preview. Google is pushing it straight into consumer apps, developer tools, and business workflows.

For developers, the biggest angle is coding agents. Google says 3.5 Flash can help transform old codebases, build apps, create interactive graphics, generate UI concepts, and even coordinate subagents through Antigravity. One demo had agents synthesize the AlphaZero paper and build a playable game in six hours.

For businesses, Google is pitching this as a practical automation engine. Shopify is using it for merchant growth forecasts. Macquarie Bank is testing it on long customer onboarding documents. Salesforce is adding it to Agentforce. Ramp is using it for smarter invoice OCR. Xero is using agents for admin tasks like supplier research and 1099 tax prep. Databricks is using it to help diagnose data issues.

The consumer side is also interesting. Gemini 3.5 Flash is now the default model for the Gemini app and AI Mode in Search. It also powers Gemini Spark, Google’s new personal AI agent that can run in the background and take action under your direction. Spark is rolling out first to trusted testers, with a beta planned for Google AI Ultra subscribers in the U.S.

Google also says Gemini 3.5 was built with stronger safeguards around cyber and CBRN risks, plus interpretability tools to better understand the model’s internal reasoning before it answers.

One more thing: Gemini 3.5 Pro is coming next month and is already being used internally.

Overall, this feels like Google moving Gemini deeper into the agent era. The headline is not just “better model.” It is “faster model that can operate across apps, code, Search, business tools, and personal workflows.”

Source: https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-5/

Anthropic upgraded Claude Managed Agents with self-hosted sandboxes and MCP tunnels, making the platform much more enterprise-friendly. The big idea: Claude can still manage the agent loop, but the actual tool execution can happen inside infrastructure the company controls.

Self-hosted sandboxes let agents run tools, access files, install packages, execute code, and do compute-heavy work without sensitive repositories or internal services leaving the company’s perimeter. Companies can use their own infrastructure or managed sandbox providers like Cloudflare, Daytona, Modal, and Vercel.

The second big feature is MCP tunnels. These let Claude agents connect to private MCP servers inside a company’s network without exposing them to the public internet. That means internal databases, private APIs, ticketing systems, knowledge bases, and other tools can become available to agents through an encrypted outbound connection.

The trust angle is the real story. Enterprises want agents that can use internal systems, but they do not want to hand over sensitive files, credentials, or private infrastructure. This update gives companies more control over runtime, networking, audit logs, resource limits, and security boundaries.

Source: https://claude.com/blog/claude-managed-agents-updates

SpaceX reportedly expects to move forward with its acquisition of Cursor about 30 days after SpaceX begins public trading, according to Bloomberg. The IPO is expected around June 12, which would put a possible Cursor deal timeline sometime in July.

This builds on earlier reporting that SpaceX had secured the option to either acquire Cursor for around $60 billion or pursue a smaller partnership instead. Reuters previously framed the Cursor deal as one of the stranger but most important parts of SpaceX’s IPO story because it ties Musk’s space company directly to AI coding infrastructure.

The logic is pretty clear: Cursor is one of the most important AI coding tools right now, and Musk’s companies need massive software automation across SpaceX, Tesla, xAI, Starlink, robotics, chips, and possibly orbital compute. Buying Cursor would give SpaceX a direct role in the developer-tool layer of the AI race.

It also makes SpaceX’s IPO even more unusual. Investors are not just being asked to value rockets and satellites. They are being asked to value a company that could include space launch, Starlink, xAI, AI infrastructure, and now potentially one of the hottest AI coding platforms.

Source: https://www.bloomberg.com/news/articles/2026-05-19/spacex-is-said-to-plan-to-buy-startup-cursor-30-days-after-ipo

SpaceX is suddenly much more than a rocket and satellite company. It is now being pitched as a massive AI infrastructure play, with investors reportedly preparing for one of the biggest IPOs in history.

The company is targeting a raise of around $75 billion at a valuation near $1.75 trillion, which could make it the largest stock market debut ever. BlackRock has reportedly discussed investing $5 billion to $10 billion in the offering.

SpaceX shareholders also approved a 5-for-1 stock split, lowering the fair market value per share from $526.59 to $105.32 ahead of the planned listing. The company is reportedly aiming to list on Nasdaq as early as June 12, 2026.

The bigger story is AI. SpaceX now includes xAI, Starlink, rocket launches, and Musk’s long-term vision for orbital data centers. Reuters’ Breakingviews framed SpaceX as four buckets: rockets, Starlink, xAI, and “Musk’s imagination.”

That imagination is doing a lot of work. Investors are not just betting on launches and satellite internet anymore. They are betting that SpaceX could become a full-stack infrastructure company for AI: chips, data centers, satellites, orbital compute, energy, and possibly space-based AI systems.

Video URL: https://youtu.be/j-FWwcnDC9k?si=zN_aRTgZ6weT453B

OpenAI added remote connections for Codex, letting developers control Codex from another device, including the ChatGPT mobile app. You can connect your phone to a Codex host, like a Mac, and keep working with the same projects, files, credentials, plugins, browser setup, and local tools.

The main idea is that Codex is becoming a long-running coding worker. From your phone, you can start or continue threads, send follow-up instructions, approve commands, review diffs, check test results, see terminal output, and get notified when Codex needs attention.

OpenAI also supports remote development environments through SSH hosts. That means Codex can work against files and shells on a remote devbox, not just your local machine. OpenAI warns teams to use normal SSH security practices like trusted keys, least-privilege accounts, and avoiding public unauthenticated access.

A useful detail: if you want Codex reachable for longer-running work, OpenAI recommends using an always-on Mac or remote host. If your Mac sleeps, loses network, or closes Codex, remote access stops.

Source: https://developers.openai.com/codex/remote-connections

Anthropic acquired Stainless, a developer tools startup that builds SDKs, CLIs, and MCP server tooling. In simple terms, Stainless helps turn APIs into clean developer libraries and connectors that apps — and AI agents — can actually use.

This matters because the next AI race is not just about smarter models. It is about agents that can reliably connect to real systems: databases, SaaS tools, internal APIs, coding platforms, enterprise apps, and workflows.

Stainless has powered every official Anthropic SDK since the early days of the Claude API, and hundreds of companies use it to generate SDKs across languages like TypeScript, Python, Go, Java, and Kotlin.

The bigger angle is MCP. Anthropic created the Model Context Protocol to help agents connect to outside tools and data. Stainless strengthens that layer by making it easier to build the connectors, SDKs, and interfaces agents need to act beyond a chat window.

Source: https://www.anthropic.com/news/anthropic-acquires-stainless

Cursor released Composer 2.5, a new version of its coding model that is now available inside Cursor.

The main upgrade is sustained work. Composer 2.5 is better at long-running coding tasks, following complex instructions, communicating clearly, and knowing how much effort to spend on a task. Cursor says it improved both intelligence and behavior, not just benchmark scores.

Composer 2.5 is built on the same open-source base checkpoint as Composer 2, Moonshot’s Kimi K2.5, but Cursor improved it with more difficult training tasks, new reinforcement learning methods, and much more synthetic coding data.

One big training change is targeted RL with textual feedback. In simple terms, instead of only telling the model whether a whole long task succeeded or failed, Cursor can now give feedback at the exact moment where the model made a bad tool call, confusing explanation, or style mistake.

Composer 2.5 was also trained with 25x more synthetic tasks than Composer 2. Cursor says this helped make the model stronger, but also created new reward-hacking issues, like the model finding hidden caches or decompiling bytecode to recover deleted information.

Pricing starts at $0.50 per million input tokens and $2.50 per million output tokens. There is also a faster version at $3 per million input tokens and $15 per million output tokens, which is now the default.

Source: https://cursor.com/blog/composer-2-5

Mistral CEO Arthur Mensch warned that Europe has about two years to build its own AI infrastructure before it becomes permanently dependent on U.S. tech giants. He made the comments during a French National Assembly hearing on digital sovereignty and AI.

His main point: the AI race is no longer just about who has the best model. It is about who controls the chips, data centers, energy, cloud platforms, and deployment pipelines behind those models. If Europe keeps importing all of that from American companies, it loses leverage.

Mensch argued that Europe needs its own sovereign AI stack, including models, GPU compute, cloud capacity, and energy infrastructure. Mistral is trying to position itself as Europe’s AI champion, but even Mensch admitted its goal of reaching a gigawatt of AI compute by 2029 is still not enough on its own.

He also criticized Europe’s fragmented capital markets and regulation, saying they make it harder for startups to scale fast enough against U.S. giants. That is a major problem when OpenAI, Anthropic, Google, Microsoft, Amazon, Meta, and xAI are all locking up massive compute deals.

Source: https://www.businessinsider.com/mistral-ceo-warns-europe-2-years-avoid-us-ai-dependence-2026-5

Microsoft AI CEO Mustafa Suleyman says AI could automate most computer-based professional tasks within the next 12 to 18 months. He specifically pointed to work in accounting, law, marketing, project management, and software engineering as areas where AI could reach human-level performance very soon.

The bigger point is not that every office worker disappears overnight. It is that many tasks done by people sitting at a computer — writing, analyzing, coding, planning, reporting, organizing, reviewing documents — could become automatable much faster than companies and workers expect.

Suleyman is also pushing Microsoft toward more AI self-sufficiency. Microsoft still works closely with OpenAI, but it is building its own foundation models, agents, and infrastructure so it is not fully dependent on one partner.

This fits the broader AI shift: companies are no longer just adding AI assistants. They are trying to build AI systems that can operate inside real workflows, handle repetitive office tasks, and eventually act like specialized digital workers.

Source: https://fortune.com/article/why-microsoft-ai-chief-mustafa-suleyman-predicts-ai-automation-18-months/