Cars Will Become "Large-Scale AI Agents”

Cars Will Become "Large-Scale AI Agents”

The automotive industry is undergoing a fundamental transformation as artificial intelligence evolves from a supporting technology to the core of vehicle design, marking the dawn of the "AI-defined vehicle" era, according to Horizon Robotics President Chen Liming.

Speaking at the World New Energy Vehicle Congress in Haikou, Chen outlined how the industry has progressed from hardware-defined to software-defined vehicles and is now entering an AI-defined phase.

In this new paradigm, intelligent cockpits and autonomous driving systems will no longer develop independently but will converge into what he termed "cabin-driving fusion," ultimately transforming vehicles into large-scale AI agents operating in the physical world.

Three Key AI-Driven Transformations

Chen identified three critical areas where AI is reshaping the automotive landscape:

Computing Power and Strategic Implications

These technological advances are creating new demands for distributed computing architectures. "As algorithms evolve and models grow larger, relying on a single vehicle or a single chip is no longer enough," Chen emphasized.

He highlighted the need for seamless coordination between vehicles, infrastructure, and cloud systems, with cloud-based training and inference complemented by low-latency edge decision-making.

Regarding strategic approaches, Chen predicted most automakers will transition from "full-stack self-research" to "full-stack controllability," leveraging ecosystem partners for cost and performance advantages as technologies mature.

He estimated approximately 20% of OEMs might maintain full-stack development capabilities, while the majority will rely on partner ecosystems for scale.

Safety as Foundation

Throughout these transformations, Chen stressed that safety remains the non-negotiable foundation. "Safety is the '1'; every other function is a zero that follows," he stated. "You can never trade safety for cost. All cost optimization must be built on a foundation of safety."

The convergence of AI and automotive technologies represents not just an incremental improvement but a fundamental redefinition of what vehicles are and how they interact with their environment, positioning AI-defined vehicles as the next major computing platform in the physical world.

‘Paid’ Stands Out with $21M Seed for Unique Billing Model

Manny Medina, founder of the sales automation company Outreach, has launched a new startup called Paid that is attracting significant investor attention.

The London-based company has raised a $21.6 million seed round led by Lightspeed, bringing its total funding to $33.3 million and valuing it at over $100 million, even before its Series A.

The Problem: Traditional Pricing Fails for AI Agents

Paid addresses a critical challenge in the AI industry: how to charge for AI agents. Traditional SaaS per-user fees or unlimited-use models are unsustainable for agent-makers, who face variable usage costs from cloud and model providers like OpenAI.

This can quickly drive them into debt.

The Solution: Charge for Value, Not Usage

Paid’s innovation is a "results-based billing" platform. Instead of charging for access or compute time, it enables AI companies to bill their customers based on the tangible value their agents create.

As Medina explains, agents often work silently in the background. "If you’re a quiet agent, you don’t get paid," he says. Paid provides the infrastructure for agents to "charge for the additional work that the agent is doing," such as a percentage of costs saved or revenue generated for the client.

Market Reception and Investor Confidence

This new model is gaining traction. Paid's early customers include Artisan, a viral sales automation startup, and enterprise software giant IFS.

Investor Alexander Schmitt from Lightspeed, which has invested over $2.5 billion in AI, believes Paid solves a core industry problem.

The funding round also included participation from FUSE and existing investor EQT Ventures, signaling a strong belief that results-based billing could be the key to unlocking widespread, profitable adoption of AI agents.

Delinea Debuts Open-Source MCP Server to Protect Agents

Delinea has introduced an MCP (Model Context Protocol) Server designed to act as a secure bridge between AI agents and the company's privileged access management platform.

The server enables AI tools to retrieve and use credentials safely by enforcing identity verification and access policies, thereby preventing unauthorized use and simplifying integration without the need for custom code.

According to Phil Calvin, Delinea's Chief Product Officer, the system is engineered to minimize the risk of credential misuse by AI.

How It Enhances Security?

Instead of granting AI systems direct access to credential vaults, the MCP Server functions as a controlled intermediary.

It exposes only specific, predefined functions, allowing AI agents to perform necessary tasks without ever handling raw usernames or passwords.

Access is tightly governed by policies and scope controls, and all authentication is performed using short-lived, ephemeral tokens.

Addressing the Core Challenge

This approach solves a critical security gap. Many AI agents require access to live systems like databases or cloud services, but hardcoding credentials into them makes auditing and access revocation difficult.

The MCP Server mitigates this by centralizing control and supporting standards like OAuth. It also includes ready-made connectors for major AI platforms such as ChatGPT, Claude, and VSCode Copilot.

Implementation and Adoption

Calvin acknowledged that integrating the server into complex, existing workflows is not a "plug-and-play" process and requires careful configuration to ensure AI agents use the new controlled tools.

To facilitate adoption, Delinea provides Docker images, comprehensive documentation, and sample integrations to help organizations deploy the solution step-by-step with confidence.

The Delinea MCP Server is now available on GitHub.

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