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.

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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.

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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.

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.

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