Bloomberg reports (paywalled) that Sam Altman told OpenAI employees the company is open to slowing the pace of AI development alongside other leading labs as concerns grow over increasingly capable systems and recent incidents in which models escaped human control. OpenAI has already paused development once this year for security work and is now pushing for mandatory U.S. AI safety requirements. Anthropic has also signaled interest in coordinating on the pace of new releases. Here are some of the details Bloomberg reported, as summarized by Reuters: - Altman told employees in a company-wide meeting that the ChatGPT maker could pace development alongside other AI labs, but some may not agree, the report added. - Safety warnings from AI researchers this week, along with several recent incidents where AI models from developers including OpenAI escaped human control, have prompted alarm and calls for tighter safety regulations. - Jacob Coxon, a former Anthropic and OpenAI researcher, publicly accused the companies earlier this week of racing toward AI advancements without acting responsibly. - An Anthropic spokesperson said on Thursday said that the company is interested in working with the AI industry on the pace of releasing new AI tools. - OpenAI said in July that AI acceleration for frontier model development may be so high that the world will "need to pace the rate of AI advancement" at some point in the future. - In August, OpenAI paused much of its model development for two weeks to bolster its defenses after its AI agents escaped containment and hacked open-source platform Hugging Face. - OpenAI said on Wednesday that it was pushing for mandatory national AI safety requirements in the United States, citing concerns that advanced AI systems could accelerate its own development.

Read more of this story at Slashdot.

It’s the end of an era at Microsoft: Frank X. Shaw, the executive who oversaw the tech giant’s communications for nearly three decades, first at an external agency and for the last 17 years as one of its senior leaders, is leaving at the end of the year.

Shaw, 64, said he’s not retiring, although he doesn’t have another job lined up. He plans to stop working for a while, do some of the things he hasn’t had time for, and then decide what’s next.

“I have had a ringside seat at some of the biggest leadership, technology, and business transformations that have ever taken place,” Shaw said, sharing the news of his departure (under embargo) in a phone call Thursday afternoon. “I just feel incredibly fortunate.”

He said he had been discussing his potential departure for some time with Takeshi Numoto, Microsoft’s chief marketing officer, looking for the right moment.

Microsoft has not announced a successor for his role as chief communications officer. In a LinkedIn post, Shaw said the company will consider internal and external candidates.

A statement from Shaw’s colleagues in corporate communications credited him for his many years shaping Microsoft’s “voice and reputation with intelligence, candor and wit. His leadership and contributions to the company are too extensive to list, as is the number of journalists who have, at one point or another, used his name in vain.”

A former Marine Corps public affairs officer, Shaw has worked with all three of Microsoft’s CEOs. He started on the agency side, at Waggener Edstrom — now known as We. Communications — when Bill Gates was still running the company.

He built his reputation defending and advocating for Microsoft through some of its hardest stretches: the antitrust years, the Windows Vista backlash, the scramble to replace Steve Ballmer as CEO, and the weekend in 2023 when OpenAI’s board fired Sam Altman.

As the company’s top communications executive, he has also told the story of Microsoft’s reinvention under CEO Satya Nadella, from the LinkedIn and Activision Blizzard deals to an AI push that has carried Azure past $100 billion in annual revenue.

Evolving with technology: Shaw has spent much of his career closely watching the tech landscape and moving Microsoft’s voice into new channels as they emerged.

“We’re always thinking about what is the art and science of communications,” Shaw told PRWeek. “How do we reach our audiences most effectively in a changing environment?” He called the arc from print to radio and TV to social media and newsletters a “constant evolution of influence.”

He turned the corporate blog into a place where the company argued its own case, writing “Microsoft by the numbers” himself in 2010 — a stat-by-stat comparison against Apple and Google that TechCrunch dubbed “fantastic passive-aggressive.”

He and his team experimented with different and risky methods of telling the company’s story, holding mass briefings under embargo and publishing documents known as the “Book of News” in advance of its major keynotes and conferences. The prospect of a reporter having to answer to “fxs” was no doubt a factor in ensuring the news (mostly) didn’t leak.

Shaw hired Steve Clayton out of a technical role at Microsoft in London, where he had been blogging about the company unofficially out of frustration with how it was perceived, and made him chief storyteller. In the middle of the AI boom, Clayton and Shaw embraced the analog undercurrents in popular culture and launched Signal, a quarterly Microsoft print magazine for business leaders.

Clayton was VP of communications strategy by the time he left in January to become chief communications officer at Cisco, making Shaw’s planned departure the second high-profile exit from Microsoft’s comms team in a year.

Adapting to AI: In recent years, Shaw made his own team a testing ground for AI, publishing what worked and what didn’t. In a 2023 post he described using Copilot in Teams to pull story ideas out of conversations with spokespeople and anticipate coverage after interviews, and asking the AI to “poke holes in a statement we’re making on a tricky topic.”

He called it his corporal, a reference to Napoleon, who was said to bring one to meetings and ask whether his generals’ war plans made sense to him. A survey of 80 people in Microsoft’s communications and marketing organization found 84% did not want to go back to working without it.

Shaw was also known to use AI as a sounding board when a story frustrated him, offering him an objective take before he called and let a particular reporter have it.

He announced his departure Friday morning in a message to Microsoft’s communications team (reminding them he’s still there for a few months yet) and his public post on LinkedIn.

“Thank you as well to all the reporters, editors, writers, influencers and analysts who have put up with me over this time, enduring my early and late night calls, my off the record ‘no comments,’ my bad story ideas and my extended commentary on headlines and positioning,” he wrote.

“You all have incredibly hard and valuable jobs,” he added, “and while I’ve not agreed with everything said about us 😊 I appreciate you anyway.”

Roblox will allow creator to deploy their games across PC< consoles, and mobile as standalone apps

In a Slack message seen by TechCrunch, Matt Mullenweg told Automattic employees he’s back in control of the company, days after its board put him on leave. Automattic has not yet confirmed the apparent reversal.

The most consequential AI news of the past year came from a standards body. In December 2025, Anthropic donated the Model Context Protocol to the newly formed Agentic AI Foundation, a directed fund under the Linux Foundation cofounded by Anthropic, Block, and OpenAI, with support from Google, Microsoft, AWS, Cloudflare, and Bloomberg. Six months earlier, Google had handed its Agent2Agent protocol to the same foundation family. Companies that compete fiercely on models are now cooperating, formally and under neutral governance, on the interfaces between them.

For three years, the agent story has centered on capability: Models got better at planning, tool use, and long tasks. Integration improved more slowly. Every agent was still wired to its tools, data sources, and host application with custom glue, leaving even successful systems difficult to move or reuse. Standard interfaces change the economics of that work. Networking became an ecosystem when machines could agree on interfaces; programming tools followed the same path with the Language Server Protocol. In my judgment, the standardization now underway will influence what engineering teams ship over the next three years as much as further gains in model capability.

The Model Context Protocol (MCP) standardizes how an AI application connects to tools and context. It uses JSON-RPC messages between hosts, clients, and servers. Servers can expose tools that a model invokes, resources containing context and data, and prompts that describe templated workflows. The spec credits the Language Server Protocol as an inspiration. Language servers replaced a separate integration for every editor-language pair with one interface on each side; MCP applies the same idea to AI applications and integrations. The current spec is dated July 28, 2026, the fifth dated protocol revision since the project launched in November 2024. That pace reflects active governance and warns implementers to expect movement.

Adoption has spread across competing hosts. The MCP maintainers’ December 2025 announcement cited more than 97 million monthly SDK downloads, over 10,000 active servers, and first-class client support across ChatGPT, Claude, Cursor, Gemini, Microsoft Copilot, and Visual Studio Code. Any individual count can be debated. Cross-vendor implementation is harder to dismiss, because a shared interface becomes infrastructure when rivals support it independently.

MCP covers the boundary between an application and its tools. The Agent2Agent protocol (A2A) addresses the next boundary: agents discovering and communicating with one another across vendors. It reached v1.0 in April 2026 with more than 150 supporting organizations, signed Agent Cards for verifiable identity, SDKs in five languages, and general availability in Microsoft Copilot Studio, Azure AI Foundry, and Amazon Bedrock AgentCore. A2A is younger and less proven in production than MCP, but its governance and adoption are moving agent-to-agent communication beyond the confines of a single platform.

The emerging stack extends beyond those two protocols. AGENTS.md, OpenAI’s convention for giving coding agents repository-level instructions, joined MCP as a founding project of the Agentic AI Foundation. The OpenTelemetry generative AI semantic conventions are developing a shared vocabulary for traces and metrics from model and tool calls, though that work has yet to reach stable status. Connection, cooperation, instruction, and telemetry are beginning to acquire common interfaces.

For an engineering organization, the immediate consequence is reuse. An MCP server for an internal ticketing system can serve every compatible IDE, chat application, and agent. Models and hosts will keep changing; the interface contract, server implementation, schemas, and authorization wiring can endure. The integration becomes an asset shared across applications. Language servers followed the same path as editors came and went around them. Agent integrations deserve the same treatment as libraries and services, with owners, versions, tests, and upgrade policies.

Reuse also concentrates attention on a common security boundary. Custom integrations tend to receive separate reviews, when they receive them at all. A shared protocol creates a recognizable seam where teams can specify identity, permissions, data flow, and audit behavior, then apply those controls across many tools and hosts. The MCP authorization specification builds that seam from established IETF work, including OAuth 2.1, protected resource metadata, authorization server metadata, and resource indicators. It requires Proof Key for Code Exchange (PKCE) to protect authorization codes from interception. MCP servers must also reject tokens issued for another audience and must not forward them downstream. The value comes from familiarity: Teams can draw on years of OAuth deployment experience instead of rediscovering its failure modes inside a new protocol.

The spec is equally useful when it states what metadata cannot prove. Tools can carry behavioral annotations describing them as read-only, destructive, idempotent, or open-world. Those terms give hosts a vocabulary for policy. The tools specification still requires clients to treat annotations as untrusted unless they come from a trusted server. Self-description can inform a security decision; it cannot establish the trust on which that decision rests.

Once servers share an interface, they also become discoverable. The MCP Registry is an open catalog and API for public servers, designed to feed downstream marketplaces and private catalogs. Its role resembles npm or PyPI as a discovery mechanism, with an important difference: It stores standardized server metadata and leaves package distribution elsewhere. The registry remains in preview, with possible breaking changes and no durability guarantee.

Discovery brings familiar supply-chain risks to components with unusually powerful access. Typosquatting, abandoned packages, malicious updates, and uncertain provenance now concern software that may hold live credentials and act on production systems. The registry provides namespace verification and moderation, and its downstream model allows organizations to build curated catalogs. An internal subregistry or allowlist is therefore a sensible first control. The standard interface makes that curation practical across multiple hosts.

The same contract improves testing. MCP tools declare a JSON Schema for their inputs and may declare one for structured outputs. Under the current tools specification, servers that declare an output schema must return conforming structured results, and clients should validate them. Teams can test a server without putting a model in the loop, mock it with recorded or synthetic behavior, and contract-test both sides as they would a REST or gRPC boundary.

This separates two kinds of uncertainty that agent evaluations often mix together. Protocol tests can determine whether an integration exchanged valid messages and enforced its contract. Model evaluation can focus on whether the agent chose the right tool and interpreted the result well. OpenTelemetry’s emerging conventions extend this approach to runtime evidence by giving systems a common language for tool calls. Teams will still have to absorb changes as those conventions mature.

These benefits stop at the edge of what the interfaces describe. A schema captures the shape of a tool’s arguments, while its meaning still lives largely in a free-form description that a model must interpret. A schema cannot tell an agent when a tool is appropriate, how its effects interact with other tools, or whether two similarly named operations have equivalent semantics. Portability makes a server available across hosts; behavior can still vary across models and contexts.

The semantic gap leads to a trust gap. MCP can carry a server’s claims, and an A2A Agent Card can carry a digital signature, but a signature only ties a statement to an identity. Engineering organizations still need a basis for deciding which identities, publishers, code, and claims deserve authority. The protocol can carry that decision through a system. It does not make the decision for the organization.

Delegation makes the problem harder. MCP’s authorization model handles a client calling a server with an audience-bound token. Production systems increasingly involve an agent calling another agent, which calls a tool or a third agent. Preventing token passthrough closes a serious hole, yet each downstream hop still needs a narrower grant derived from the user’s original authority. No common mechanism defines how those rights should attenuate across an arbitrary chain. Platforms currently solve this locally or leave too much authority in place.

All of this work is unfolding on young infrastructure. The registry is in preview, the telemetry conventions are unstable, and MCP has produced five dated protocol revisions in less than two years. Revision is how standards mature, so teams should version-pin, keep protocol code behind thin internal adapters, and budget for migrations. Some abstractions will prove wrong because the standards and the underlying practice are developing at the same time.

A sensible response begins with ownership and containment. Each internal server needs a durable owner, and its protocol surface should be treated as an architecture decision. Schema validation, conformance tests, and protocol mocks put that seam under contract. Third-party servers belong behind an allowlist or private catalog, with provenance requirements and corroboration for their annotations. An internal abstraction around the emerging telemetry vocabulary can limit exposure to draft revisions.

Organizations with a large stake in agents should also participate in the standards work. The Agentic AI Foundation and the A2A project are young enough that engineering teams outside the founding companies can still influence what becomes portable, observable, and enforceable. The rules written now will become assumptions embedded in future products.

Model improvements will continue to generate the headlines. Standards determine whether those models can participate in an ecosystem. Ethernet mattered because it became an interface that many implementers could agree on, and language servers mattered because editors and language tools no longer needed to be designed in pairs. Agent systems now have their first interfaces that competitors jointly govern and independently implement. Engineering teams should build on them with two expectations: The interfaces are likely to last, and their current forms will change.

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