The startup landscape in 2026 is being reshaped by a new generation of companies building for the realities of an AI-driven economy. Rather than simply adding AI features to existing products, these startups are creating infrastructure for AI agents, rethinking how enterprises manage cybersecurity, addressing the energy demands of data centers and transforming industries ranging from healthcare to freight rail.

These are 10 startups worth watching as they develop products around some of the biggest technology shifts underway.

Hud: Runtime Intelligence for Coding Agents

As AI coding agents become increasingly capable, the challenge is shifting from generating code to safely fixing what happens when that code runs in production. Hud is building a runtime intelligence layer designed specifically for this new environment.

Hud runs alongside production code, mapping the entire codebase without configuration and detecting errors, performance degradations and CPU spikes. When an issue occurs, the platform captures deep forensic context about what actually happened and sends that information to AI coding agents so they can generate safer, code-level fixes. The company argues that traditional observability was designed for humans analyzing historical data, while runtime intelligence is built for coding agents that need precise context to act.

Velocity: Building the Growth Engine for the AI Era

The rise of conversational AI is creating a new frontier for digital advertising, and Velocity is building infrastructure to support it. The company describes its platform as an AI-native monetization and distribution system connecting developers with advertisers and real-time, intent-driven audiences.

Velocity analyzes a user's message and conversation context to identify relevant advertisers, offers and messaging. It then generates a tailored ad experience in milliseconds and delivers it natively within the conversation. Its platform also includes real-time auctions, optimization tools and a conversation abstraction layer designed to extract intent while filtering personally identifiable and sensitive information.

Voltify: Building an Energy Network for Freight Rail

Freight rail is a massive energy consumer, and Voltify is targeting the infrastructure behind it. The company is developing an energy system that combines battery-electric locomotive retrofits, autonomous on-route charging and AI-optimized microgrids powered by renewable generation.

The idea extends beyond trains. Voltify's microgrids can also provide grid-independent energy to factories, mines and rail yards along the same corridors, effectively turning existing rail infrastructure into a broader energy network for industry.

Artemis: Defending Companies in the AI Era

As cyberattacks become faster and more automated, Artemis is taking an AI-native approach to security operations. Its platform is designed to help organizations identify genuine threats while addressing the noise, cost and limited context associated with traditional security information and event management systems.

Artemis says its technology processes more than 2 billion events every hour and more than 15,000 terabytes of data daily, generating over 2,000 insights each day. Its focus is on delivering precise detections with actionable context and adaptive coverage as the threat landscape evolves.

LearnVector: Rethinking How People Learn With AI

Andrew Ng's LearnVector is entering the market with an ambitious premise: AI should not simply replace human teachers or give people faster answers, but fundamentally change how people learn. The company's mission is to accelerate human development by making high-quality, personalized learning more accessible.

LearnVector argues that traditional education remains largely a one-to-many experience, while AI can enable a shift toward one-to-one learning. Its vision is to make learning more personal, engaging and continuous, moving education from an occasional activity toward a daily habit.

Claros: Tackling the Energy Challenge Behind AI

The rapid expansion of AI is putting unprecedented pressure on data center infrastructure, particularly the power systems required to support increasingly dense computing environments. Claros is developing hardware and software designed to address that challenge.

Its power management approach aims to enable higher compute density, improve power delivery and increase the efficiency of data centers and AI applications. By reducing energy consumption and the complexity associated with advanced power systems, Claros is positioning its technology as part of the infrastructure needed to support the next phase of AI growth.

Freehand: Bringing AI Teams to Complex Spend Management

Procurement and supply chain operations remain heavily dependent on manual processes, even as enterprises adopt increasingly sophisticated financial and enterprise software. Freehand is using AI teams to tackle the gap between those systems and the work employees still perform around them.

The company says its AI teams can audit invoices, enforce contracts, resolve disputes and post results directly to enterprise resource planning systems. Initially focused on freight, Freehand's platform is designed to turn complex spend management into an automated workflow while keeping humans in control.

Clay: Building the Infrastructure for AI-Native Growth

Go-to-market teams are increasingly turning to AI to identify prospects, research accounts and personalize outreach, but those systems depend on access to high-quality data and the ability to orchestrate workflows across multiple tools. Clay is positioning itself as infrastructure for this emerging category.

The platform brings together data from more than 200 data and AI vendors, enables companies to create custom intent signals and allows GTM teams to build agents that research and qualify accounts. It also connects workflows across CRM, data warehouses and other GTM systems, giving teams a common data layer for launching new revenue plays.

Sprinter Health: Bringing Preventive Care Into the Home

Healthcare is another industry being reshaped by the shift toward more personalized and accessible services. Sprinter Health is building a hybrid model that combines in-home visits with virtual care to expand access to preventive and proactive healthcare.

Its Sprinter Care+ visits bring clinical and preventive services directly into patients' homes, while a virtual team of physicians, nurses, pharmacists and care navigators provides additional support. The company employs W-2 phlebotomists from the communities they serve, training them in medical assistant and community health worker skills to connect patients with broader care teams.

Neo: Securing the Agentic Enterprise

As companies deploy AI agents across their environments, security teams face a new problem: understanding not only which software is running, but what those systems are doing in real time. Neo is building a security platform around that challenge.

The company aims to give SecOps teams visibility into agents, plugins, extensions, MCP servers and other agentic applications across enterprise endpoints. Its platform is designed to enforce granular controls over tool calls, API access and data movement while attributing actions to the human or agent responsible for them. With data remaining on the device, Neo is also positioning privacy as a core part of its approach to securing the agentic enterprise.

The Startups to Watch Next

The companies emerging in 2026 reflect a broader shift in the startup ecosystem. The opportunity is increasingly moving beyond building another AI application and toward creating the infrastructure, security, energy systems and workflows required for an economy in which AI is deeply embedded in how people and businesses operate. For investors, enterprises and technology leaders, the startups building these foundations may ultimately prove to be among the most consequential companies of the decade.

This story was published on HackerNoon under our Business Blogging Program”