This week during an interview with Bloomberg, Jensen Huang made quite the prediction.
The Nvidia CEO said the semiconductor industry will need to grow roughly five to tenfold over the next decade to support AI agents and robots to support what he believes is the next wave of computing. Huang believes that future demand will come from autonomous software agents and physical robots consuming compute around the clock.
“In the future, we have AI agents and robots, and they will be using computers,” Huang said. “Instead of a billion people using computers, we will have 100 billion agents and billions of robots all using computers. The computer industry built on top of the chip industry is certainly not big enough. Computers are being built not just for people to use, but computers are being built for computers to use.”
“Instead of a billion people using computers, we will have 100 billion agents and billions of robots all using computers. The computer industry built on top of the chip industry is certainly not big enough.”
Agents replace human endpoints
The 5-10x forecast — which Huang framed as his personal estimate, not a certainty — builds on a message he has been repeating for months, including a recent appearance where he declared traditional coding dead in favor of engineers who build AI agents. Still, it reflects the need to build backend systems for AI agents and machines, something infrastructure teams are already contending with.
On Nvidia’s fiscal Q1 2027 earnings call in May, Huang described the move from generative AI to agentic AI — systems “capable of perceiving, reasoning, planning, and acting” — as the next major phase of the industry.
South Korea’s infrastructure role
As API requests come from AI agents more often, standard assumptions around rate limiting, session memory, sub-millisecond inference routing, and API gateway concurrency are starting to break down. An environment in which most traffic originates from autonomous background loops rather than human thumbs changes how backend infrastructure must be built from the ground up.
To support an endpoint explosion of this scale, the physical supply chain must scale dramatically at the memory and data center layers. Speaking at the AI Summit in San Francisco on July 24, Huang pointed to South Korea as an important linchpin of the global AI buildout. “This is truly the beginning of a golden age for Korea,” he said, noting that the country’s semiconductor and industrial capabilities position it to help the world build out AI infrastructure.
“This is truly the beginning of a golden age for Korea.”
SK Group’s $500 billion bet
To back that vision, Nvidia announced a comprehensive partnership with SK Group valued at over $500 billion. The initiative spans massive purchasing of next-generation High-Bandwidth Memory (HBM) from SK Hynix, jointly co-developing custom HBM4 roadmaps designed specifically for agentic and physical AI workloads, and deploying Nvidia supercomputers.
The announcement also included major infrastructure investments across South Korea. SK Telecom said it plans to build a 2-gigawatt AI data center using Nvidia’s Vera Rubin architecture and SK Hynix’s HBM4 memory, with the first facility expected to come online in 2027. At the same time, Nvidia will invest $1 billion in Naver Corp, with Brookfield funding up to $9 billion as the project’s infrastructure capital partner, to help expand the company’s AI data center capacity from 55 megawatts to 200 megawatts by 2028.
Locking up supply early
Huang’s prediction also helps explain why Nvidia and other infrastructure companies are locking up supply years in advance. The company recently disclosed $119 billion in supply-related commitments as it works to secure everything from advanced packaging capacity to power, land, and high-bandwidth memory.
“Computers are being built not just for people to use, but computers are being built for computers to use.”
Huang believes the industry needs to stop thinking about a world where computers primarily serve people and start planning for one where AI agents and robots generate much of the demand. In his view, the ultimate limiting factor will be whether the industry can build enough physical infrastructure to keep up — a constraint already reshaping how companies like Nvidia and Palantir approach sovereign AI deployments.
Every major security vendor now has an AI copilot, but Mate Security thinks they’re solving the wrong problem.
The Tel Aviv-based startup announced on Tuesday it has raised a $35 million Series A led by Canaan Partners, with participation from Insight Partners, Team8 and M12, Microsoft’s venture fund, just eight months after closing a $15.5 million seed round. Mate’s pitch is that security operations need more than an LLM bolted onto a SIEM; they need a new architectural foundation built around AI.
That’s a bold claim in a market dominated by the likes of Microsoft Security Copilot, Google Security Operations, CrowdStrike Charlotte AI and Palo Alto Networks Cortex AI, all of which promise to help analysts investigate alerts faster. Mate, however, is betting the real differentiator isn’t a smarter assistant but a richer understanding of the organization itself.
Central to that vision is what Mate calls its Security Context Graph, a continuously updated model of an organization’s assets, users, business processes, and data that AI agents use to investigate alerts and make decisions with far more business context than a standalone LLM can provide.
Mate’s pitch is that security operations need more than an LLM bolted onto a SIEM; they need a new architectural foundation built around AI.
Mate CEO and co-founder Asaf Wiener tells The New Stack that the company launched with that intelligence layer, but says the product has evolved significantly over the past eight months.
“We started with the intelligence layer, the context layer that we built for enterprises in order to investigate alerts and incidents,” Wiener says. “We moved forward into the detection layer to connect the two, and now we’re heading to the security data sources.”
Mate calls the architecture Continuous Detection, Continuous Response (CDCR), linking detection and investigation so each continuously improves the other.
“We’re connecting between those two layers in the security operations center,” Wiener says. “With this architecture, we’re seeing amazing results related to the quality, accuracy and precision that we can get.”
Mate says the extra context helps its agents work out whether something that looks suspicious actually warrants attention. A burst of failed logins, for example, might look like an attack until the system spots that a security test was scheduled for the same time. Similarly, a large download of sensitive files takes on a different meaning if the employee involved is about to leave the company.
That approach appears to be resonating. Just eight months after its seed round, Mate has landed a $35 million Series A, a pace Wiener says reflects customer demand more than fundraising momentum.
“The pace is really crazy. We didn’t expect that,” he said. “We saw incredible traction with our customers. We’re talking about Fortune 500 companies, and revenue growth of more than 500 percent since Q3 2025. That’s what led those VCs to come to us and want to be part of the journey.”
“We’re talking about Fortune 500 companies, and revenue growth of more than 500 percent since Q3 2025.”
“What we are seeing is more and more data sources that we need to protect. Every employee in the organization can build new applications and new data sources. We need to build more detections for those risks, and the result: We need to investigate an increasing number of alerts every day.
“With human staff alone, we cannot handle it,” he says. “We need technology to let us scale.”
That challenge isn’t unique to Mate. Every major security platform is trying to give AI more context about the environments it’s protecting, albeit in different ways. Microsoft builds Security Copilot on telemetry flowing through Defender and Sentinel; Google ties Gemini into its security operations platform; and CrowdStrike’s Charlotte AI draws on endpoint and identity data already stored in Falcon.
Mate wants other vendors’ agents to work with its Security Context Graph, rather than keeping the technology confined to its own tools. Those agents would have access to the same information about the customer and its environment. Mate says they can remember previous investigations, while a “least-agency” model restricts what each one can see and do.
While Mate is still building out that vision, Wiener said the speed at which large companies have bought into it has caught him by surprise.
“What I’m seeing right now is that we’re doing those sales cycles in a few weeks,” he says. “That’s incredible.”
He attributes that acceleration not just to security teams, but to executives pushing AI adoption from the top. “It’s amazing to see that coming also from the board level, the CEO and the CIO that are pushing organizations to leverage this kind of technology.”
The fresh funding will primarily go toward expanding both the product and the team, although Wiener says an AI-native company scales differently from traditional software businesses.
“The plan is to double and triple the size of the team to address the demand,” he says. “But our AI builders can do much more today with the technology around us.”
Mate is still competing against security giants with deeply entrenched platforms. But if its early customer growth is any indication, investors are betting that the next generation of security operations will depend less on adding another AI assistant and more on giving those assistants a deeper understanding of the businesses they’re protecting.
AI agents can impress in a demo and still fumble in production. Diagrid’s Catalyst 2.0 aims to make them more resilient — and their actions tamper-evident — for high-stakes work.
With the launch of Catalyst 2.0, Diagrid on Tuesday has added a durable execution and attestation layer to agents built with LangGraph, Microsoft Agent Framework, Google’s Agent Development Kit, OpenAI Agents SDK, and other popular frameworks.
The point here, the company notes, isn’t to get developers to adopt yet another agent framework. Instead, Catalyst runs underneath the existing frameworks and turns the agent’s model calls, tool calls, and handoffs into steps in a durable workflow. Diagrid says this allows an agent to resume from its last completed step when it’s interrupted, without having to repeat the entire run from step one.
“If the agent gets a prompt and it chooses to run 100 tools for the job and it fails at the 99th, it really needs to start back up from 99,” Diagrid co-founder and CTO Yaron Schneider tells The New Stack.
Picking back up at tool call 99
Catalyst is built on the open source Distributed Application Runtime (Dapr), which the Diagrid team helped build at Microsoft, and its built-in workflow engine. For each supported agent framework, Diagrid provides a runner that intercepts the framework’s execution loop and registers its operations as workflow activities.
“We hooked into their agent runner lifecycle, and we’re essentially able to take the agentic steps that are being executed in real time and register them as workflow steps for our workflow engine in Catalyst,” Schneider says.
Credit: Diagrid
In a LangGraph application, for example, a developer compiles the graph as usual and passes it to Diagrid’s DaprWorkflowGraphRunner. Catalyst records the inputs and outputs of the model and tool calls. Dapr’s workflow runtime can then replay the orchestration after a crash, while returning the stored results of completed activities instead of executing them again.
It’s worth noting that for LangGraph users, this isn’t the first form of durable execution. LangGraph’s own persistence layer saves state at superstep boundaries and supports resuming from the last successful step. Its Agent Server also provides a durable task queue and persistent checkpoints.
Diagrid’s argument is that Catalyst provides the same execution model across more than 10 frameworks and extends it to individual model and tool calls, without requiring developers to build separate recovery logic for each framework. Schneider says LangGraph is “without a doubt, hands down” the most common framework among Diagrid’s customers, with AWS Strands and Microsoft Agent Framework also showing up. All the other supported frameworks, he says, are in the long tail but easy enough to support that it makes sense for Diagrid.
A signed record of the run
There is a second part to Catalyst 2.0, though, which may be just as important for many enterprise users. With this update, the tool now brings the workflow-history signing features introduced in Dapr 1.18 to the supported agent frameworks.
“We keep like a ledger, like a diary,” Schneider says. “We log the input, we log the output, we log which systems we talk to.”
He describes the result as an immutable store but also notes that Catalyst doesn’t turn an arbitrary database into a blockchain. It creates a signed history that should reveal later modification.
Dapr computes a SHA-256 digest over batches of workflow-history events, links each digest to the previous signature, and signs the result with the Dapr sidecar’s Secure Production Identity Framework for Everyone (SPIFFE) identity. It stores these signatures and certificates alongside the workflow history and verifies the chain whenever it loads the workflow state. If somebody were to modify, remove, or reorder a stored event, that verification chain breaks.
Schneider says Catalyst customers can use their own certificates and retain the encrypted history so it can be inspected even if they are no longer running Catalyst. The platform can use a customer-selected database, while the hash chain supplies the tamper evidence.
One part of the compliance problem
Diagrid is positioning that tamperproof record as useful for financial services, health care, and other regulated industries. CEO Mark Fussell says some of the financial executives the company has talked to see the lack of a verifiable record as a blocker for deploying agents in sensitive workflows.
The European Union’s AI Act is another reason Diagrid is making this argument now. Article 12 of the AI Act requires high-risk AI systems to support automatic event logging so operators can trace their behavior, identify risks, and monitor deployed systems, and a signed execution history could help with that requirement.
Fussell says Catalyst is meant to run alongside the agent services enterprises already use from the cloud providers. Teams can keep a provider’s identity, evaluation, and observability systems while using Catalyst for recovery and signed workflow history. Catalyst can run as a Diagrid-hosted service or in a customer’s environment, including air-gapped deployments.
Diagrid didn’t disclose pricing for the new release.
The Model Context Protocol, the open standard that has quietly become the connective tissue between AI agents and the world's software, is getting its largest update since Anthropic released it twenty months ago — a sweeping architectural revision that its maintainers and backers say finally makes agentic AI ready for massive enterprise production deployments.
The update, released today under the stewardship of the Agentic AI Foundation (AAIF), a directed fund under the Linux Foundation, finalizes MCP's transition to a fully stateless architecture, hardens its authentication model against a known class of attacks, establishes a formal 12-month deprecation policy, and graduates two headline capabilities — interactive server-rendered interfaces and long-running asynchronous tasks — into official protocol extensions.
The changes may sound arcane. Their consequences are anything but. According to the announcement, running MCP at scale has historically required "sticky routing" or shared state to maintain continuity across sessions — an operational burden that made large production deployments complex even when the underlying capabilities were simple. The new release removes that bottleneck entirely, letting organizations run MCP servers behind standard load balancers using the Kubernetes and cloud-native DevOps tooling they already operate.
"Some people jokingly call it a v2, and I think in spirit that's accurate," David Soria Parra, MCP's co-creator and a lead maintainer at Anthropic, told VentureBeat in an exclusive interview. "It's probably the biggest change we've ever made to the protocol, and with that, it's a big step up in maturing it for use by really big players."
Why stateless architecture is the key to running AI agents at enterprise scale
To understand why the industry's largest companies pushed for this release, it helps to understand what was broken. Under the old design, an MCP client — the AI application making requests — had to maintain a persistent session with a specific server instance. In modern cloud environments, where fleets of interchangeable compute nodes spin up and down behind load balancers, that requirement was poison. If the specific server holding your session state disappeared, your agent's work disappeared with it.
"Before, you needed to have a session store and manage session IDs — and if one of your compute pods went down, all of a sudden the requests would start failing," said Den Delimarsky, a lead maintainer of the protocol, in an interview with VentureBeat. "That's not going to be a problem with the new version of the protocol. That's a huge unlock, and it's one we collaborated with folks across many companies to put together."
Mazin Gilbert, executive director of the AAIF and a veteran of Google and AT&T, framed the change in historical terms — comparing it to the architectural decision that made the web itself possible. "That stateless capability enables your MCP client to speak to a load balancer that connects with any server. You don't need the stickiness," Gilbert told VentureBeat. "You could not have the internet we have today if my browser couldn't speak to any website — with any server supporting that connection. You can switch between servers behind a load balancer."
Gilbert said the constraint had become the primary blocker for companies trying to move AI agents from pilots into production. "I've come across companies who are deploying tens of thousands of agents, and you cannot do that without having to go in this direction," he said. Crucially, he argued, the obstacle was never the AI itself: "It wasn't the technology, it wasn't the business case, it was really these fundamental changes that were required."
The tension is nearly as old as the protocol. A public design discussion opened by MCP co-creator Justin Spahr-Summers on GitHub in December 2024 — just weeks after launch — flagged that MCP's long-lived, stateful connections were limiting for serverless deployments, and sketched three possible paths forward, including the fully stateless option the protocol has now largely embraced.
Engineers from Vercel, Cloudflare, Shopify, and Amazon weighed in over the following months, a preview of the multi-vendor collaboration that would eventually define the project. The core maintainers formally committed to the direction at a December 2025 meeting on the future of MCP transports, according to the announcement.
The trade-offs of removing state from the Model Context Protocol
Protocol design is a game of trade-offs, and the maintainers were unusually candid about what this one cost. First, payloads get bigger. "A lot of the state doesn't disappear, but it's moved back and forth with the server on the wire, at the actual transport layer," Soria Parra explained. "You get bigger payloads in return for statelessness — but luckily they're very compressible and very well understood, and still fairly small in comparison to an HTTP request on the web."
Second, a handful of rarely used capabilities are gone or narrowed. Out-of-band server logging — where a server could push informational log messages to a client at any moment — no longer works in the new model. The team did its homework before cutting it: "As part of the whole exercise, we scraped all of GitHub and looked at who is using it — and it's basically nobody," Soria Parra said. Those affected amount to "probably a handful of people — quite literally a handful of people."
He even allowed himself a moment of engineering self-deprecation. "I'm sad that things I thought were useful turned out not to be useful," he said. "I think one of the bigger trade-offs was more about my ego than any actual limitation of the protocol."
Delimarsky argued the shift is less a removal of state than a deliberate transfer of responsibility. "With statelessness, we did shift the responsibility of creating and managing state to the developers — but very intentionally so," he said. Under the old protocol, "a lot of folks had a hard time understanding: Do I need to use this? Where do I use this? How do I use this? Removing that burden basically says: look, now you can manage state in the way that makes sense for your environment."
For most developers, migration should be nearly painless, because the vast majority of the ecosystem builds on official SDKs in TypeScript, Python, C#, Rust, Java, and other languages, which will absorb the changes. "One of the key things we constantly do is double-check that the upgrade path is minimal — to the point where any model in the world will probably one-shot it for you," Soria Parra said — a telling remark in itself, reflecting an era in which protocol maintainers now design migrations to be trivially executable by AI coding assistants.
How a 12-month deprecation policy gives enterprises the stability guarantee they demanded
Perhaps the most enterprise-flavored feature of the release isn't code at all. It's a policy. The new formal deprecation framework guarantees developers a minimum of twelve months between a feature's formal deprecation and its earliest possible removal — the kind of stability contract that lets a Fortune 500 engineering organization commit to a specification without fearing silent breakage.
The number wasn't picked arbitrarily. "We consulted with folks like Google, Microsoft, and Amazon to find out: in your deployment environment, what's the right path for making these kinds of changes?" Delimarsky said. "Twelve months seemed like the reasonable middle ground." He stressed that features are not being torn out on a whim: "It's not about ripping stuff out of the protocol just because we don't like it. There's a very, very strong industry pull behind these changes."
Soria Parra added that the maintainers' own telemetry supports the figure — most of the ecosystem upgrades within six to eight months — and stressed that the window functions more as a listening period than a countdown clock. "It just says that in 12 months we are open to remove it, but both Den and I can change our minds based on feedback," he said. "I think it's more of a feedback period than a definite period."
Gilbert sees the policy as one leg of a three-legged stool of enterprise trust, alongside open standards and stateless scale. "There are companies deploying things at a smaller scale, but they're slowed down because of MCP's authorization gap, because of identity, because of — do they trust the deprecation policy? Things could change basically any day," he said. Those companies, he argued, "are going to benefit not because of the statelessness. They're going to benefit because of the security."
New authentication hardening closes OAuth mix-up attacks before hackers could exploit them
The release also ships significant authorization hardening, aligning MCP's auth specification with how OAuth 2.0 and OpenID Connect are actually deployed in practice. Most notably, the protocol now enforces mandatory validation of the issuer (iss) parameter — a protocol-level defense that, according to the announcement, closes an entire class of so-called mix-up attacks, in which a client can be tricked into associating an authorization response with the wrong identity server.
Was anyone actually attacked? No, Delimarsky said — this was preventive engineering, not incident response. "This is not something that is gated in any existing vulnerabilities or active exploitation," he said. "This is more of us engaging directly with the security community." The philosophy, he explained, is to borrow rather than invent: "MCP as a protocol is very much establishing the pattern of: we do not want to reinvent the wheel, but we also want to be at the forefront of a lot of the security innovation."
That posture is most visible in the new Enterprise Managed Authorization extension, developed in close collaboration with identity provider Okta, which lets organizations make their corporate identity provider the authoritative gatekeeper for MCP server access. "If I'm somebody that manages tens, hundreds of MCP servers for my organization, I want to make sure that I enforce some level of common governance, where folks auth with their corporate credentials and not their personal credentials, so that the client doesn't send data to sources that are unauthorized," Delimarsky said. Okta bootstrapped the underlying open standard, he noted, and the maintainers then worked "to make sure that it's adopted ecosystem-wide, and it's not something that is specific to only one vendor or provider."
More is coming: Delimarsky said proposals are already on deck for demonstrated proof-of-possession and workload identity federation — capabilities requested by security teams running MCP in production. Gilbert connected the work to a broader maturation: "MCP has now bridged that gap with these authorization protocols, so it's basically now becoming what we call enterprise ready, versus an open lab sort of experiment."
MCP Apps and Tasks become official extensions, pushing AI agents beyond text responses
Two capabilities graduate to official extension status in this release, taking advantage of a new framework that lets extensions evolve on their own timelines, independent of the core specification — a structural choice that lets the protocol grow without bloating its core.
MCP Apps allows servers to ship rich, interactive, server-rendered user interfaces directly into AI clients — moving agent output beyond walls of text toward dashboards, forms, and visualizations, and dramatically accelerating development of user-facing agentic applications, according to the announcement. MCP Tasks tackles the reality that not every tool call finishes in one round trip. Instead of holding fragile, long-lived connections open while a batch job or heavy computation grinds away, servers now return a durable task handle; clients can disconnect, crash, restart, and resume polling. "You've been processing some audio for a podcast or a video — it can notify back the client and say, hey, the task is done. You don't need to wait and keep the stream open," Delimarsky said.
A third addition, multi-round-trip requests, lets servers and clients negotiate back and forth within a single logical operation. "It's not just a one-shot — over the stream, get the input and you're done," Delimarsky said. "You can actually interact, server to client, to get the right parameters to execute an action."
Soria Parra emphasized that these capabilities emerged from the same source as the architectural overhaul: heavyweight production users. "This is a version that came together by some of the best distributed systems experts at Microsoft, Google, and others coming together and working on this for their specific needs — and the needs of the industry at large," he said.
How independent is MCP from Anthropic under Linux Foundation governance?
Soria Parra was disarmingly direct about the residual power he holds. As lead maintainer and Anthropic employee, "I do have veto rights, technically," he acknowledged — "but I think we have never actively used it in any kind of discussion."
The core maintainer group now spans Anthropic, Microsoft, OpenAI, Google, and Amazon, with contributions from companies like Block, and key decisions "are usually unanimous," he said. "Technically we have a lot of influence; de facto, we're not exerting any of it." He added that governance will progressively broaden: "As the project progresses, we will increasingly move to more different governing structures that include more and more people."
Gilbert, who has helped stand up multiple foundations during his time working with the Linux Foundation, offered the numbers behind the neutrality claim. The AAIF has grown from roughly 40 members at its December inauguration to 240 today — "the fastest growing foundation" in Linux Foundation history by membership, he said, "signing up one member every day."
Anthropic's share of contributions, by his estimate, has fallen below half. "Holding control of a project doesn't make it an open standard," Gilbert said. "You have to let go. You have to contribute, and you have to grow the pie and the community. And Anthropic has done an incredible job doing exactly that."
Notably, the foundation's membership has expanded well beyond tech vendors into retail, finance, and telecom companies — adopters who, Gilbert says, "are no longer just deploying the protocols. They want a voice, and they want to be at the table to influence the protocol from the get-go, and that's something we have not seen before." The roster now includes CERN and, tellingly, Consumer Reports — "because somebody has to defend consumers when this internet of agents comes alive."
Keeping one global AI agent standard amid US-China technology tensions
The AAIF is betting that neutrality can hold even amid geopolitical friction. The foundation will host AGNTCon and MCPCon events this fall in Shanghai, Tokyo, Amsterdam, and San Jose, with additional events planned in South Korea, Nairobi, and Toronto, and Gilbert said he is personally investing in growing membership across Asia and India, where he sees underdeveloped growth markets for the foundation.
His answer to the geopolitics question was emphatic model-agnosticism. "We're completely agnostic to what the model is, whether the model is Kimi, or Gemma, or a frontier model from Anthropic, or from anybody," he said. "Every model will have to support MCP — whether it is a Chinese model or whether it is a U.S. model, it doesn't matter. The protocols must be open, standardized."
The logic is economic as much as diplomatic. Enterprises, Gilbert argued, increasingly pick models "left, right, and center" based on the task at hand — and no model, regardless of national origin, "can provide value to an enterprise 500 customer company unless you have the protocols open, standardized." In his telling, the foundation exists precisely to provide neutral ground: a place "where competitors who compete furiously during daytime" can "come to a neutral room and debate, converse, align, consolidate, and drive open standards of how the Internet of Agents will evolve."
That framing echoes his favorite historical analogy. HTTP earned global trust, he said, because of three things: an open standard, stateless scalability, and neutral governance under a standards body. "If I were a Fortune 500 company looking at how I trust the internet, I'd need those three things to fall into place — and they were not in place a year ago. They were not in place even six months ago. But they are in place today."
What 250 million weekly SDK downloads reveal about the future of agentic AI
The scale of what's now riding on this specification is difficult to overstate. Soria Parra said SDK downloads have doubled in the past six months, reaching roughly 250 million per week — "which is just insane numbers."
For context, Anthropic reported 97 million monthly downloads across just the Python and TypeScript SDKs when it donated the protocol in December 2025. Delimarsky pointed to that same adoption curve as his preferred success metric going forward: "There is certainly a certain inflection point where this is no longer just an open source project. This is a substrate for a lot of the agentic workflows that we see across enterprises, across startups, across all sorts of companies."
Success, the maintainers say, will be measured in server counts on the new specification, in feedback flowing through working groups, GitHub discussions, and the project's Discord — and in whether the biggest drivers of the changes, Microsoft and Google among them, ship on it. "They are effectively the ones who have been driving a lot of the changes," Soria Parra said. "Every early indication we have — it looks very, very positive."
Both maintainers closed on the same note: this release belongs to no single company. "If you look back 18 months ago, when it was an Anthropic-only project, and then 12 months ago, where there was a lot of engagement — now it's a truly global community," Soria Parra said. "I'm incredibly proud of what they have worked together." Delimarsky, "being very unoriginal," seconded him: the release "would not be possible without a large community of folks that are also volunteering a lot of their own time in making MCP successful."
Gilbert, meanwhile, is already looking past this release — toward how MCP interlocks with the AAIF's newly announced Agent Gateway project for traffic management and policy enforcement, and toward agentic commerce, where MCP serves as the discovery layer letting merchants expose products and services to AI agents. The web took thirty years to become invisible infrastructure that billions trust without thinking. By Gilbert's reckoning, the internet of agents is "in its first, second year" — and as of today, it finally has plumbing built to carry the load.
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