An AI-Native Strike at the Heart of IT Services
Hang Ten Systems is an AI-native enterprise services startup founded by former Infosys CEO Vishal Sikka to replace traditional, labor-heavy IT services work with AI-driven software building, customization, and operation for large organizations at scale. This launch is not another generic AI consultancy; it is a direct commentary on decades of outsourcing economics. Hang Ten’s bet is blunt: if AI can write code, maintain systems, and adapt applications continuously, the old model of selling armies of engineers and long projects is out of date. That stance makes the company more than a new entrant. It turns Sikka’s own past leadership of a major IT services firm into a critique of the model that made him famous—and into a roadmap for something leaner, faster, and far more automated.
From Funding OpenAI to Competing With AI-First Services
Vishal Sikka’s new move is rooted in a decade-long arc. He led Infosys when it granted funds to help OpenAI get started, and now he is building his own AI play in the same space. That experience matters: he has already seen how slow the journey from promising research to deployed enterprise systems can be. After leaving Infosys, he founded Vianai Systems in 2019 as an enterprise AI company focused on human-centered tools for large organizations, with a finance-focused product called hila and partnerships with cloud and services players. Hang Ten Systems is a fresh venture separate from Vianai, but it keeps the same obsession: turning AI from a science project into production-grade AI enterprise services for real business functions. In that sense, Sikka is no newcomer experimenting on the sidelines; he is doubling down on a thesis he has been testing for years.
A $32M Seed Bet on AI Enterprise Services at Scale
Hang Ten Systems has raised a USD 32 million (approx. RM150,000,000) seed round led by Mayfield, with a strategic investment from Aramco Ventures and participation from angel investors. One quotable detail from the funding announcement is that the company already had paying customers within about a month of launch, a sign that this is not a concept-stage AI pitch. Early clients include Siemens Gamesa Renewable Energy and Fresenius, which are using Hang Ten for AI-native project delivery. Hang Ten describes its approach as “agentic code generation” plus a reusable library of AI skills and a bench of forward deployed engineers for areas like finance, HR, and new product work. The goal is clear: scale AI-native IT services through accumulated project learning, not endless hiring.
The $250B Question: Can AI-Native IT Services Replace Outsourcing?
Hang Ten is pointed directly at the software work that global enterprises have long handed to large IT services firms—customization, integration, and maintenance that historically soaked up thousands of billable hours. The broader technology industry is expected to reach about USD 315 billion (approx. RM1,450,000,000,000) in the near term, with the software services segment alone described as a roughly USD 250 billion (approx. RM1,150,000,000,000) market now under AI pressure. Public investors are uneasy; some analysts have warned that severe AI disruption could trigger valuation cuts of 30% to 65% for parts of the sector, and services stocks have already been under strain as automation tools advance. Incumbents argue AI will amplify, not replace, their businesses and are aligning with major model providers to defend their turf. Hang Ten’s existence challenges that optimism by showing that AI-native IT services can be built from the ground up, without legacy delivery baggage.
Why Hang Ten Signals a Strategic Pivot in Enterprise Delivery
Hang Ten Systems represents a structural shift from traditional consulting and outsourcing toward AI-native enterprise solutions that assume automation as the default, not an add-on. Instead of adding AI tools into existing delivery pipelines, Sikka is designing the pipeline around AI from day one: agents generate and modify code, reusable skills carry learning from one project to the next, and a smaller expert team handles the edge cases. This model turns the classic services promise inside out. The value is not in how many people a vendor assigns, but in how much work disappears into AI enterprise services that keep improving with every project. If Hang Ten succeeds, it will pressure incumbents to adopt similar architectures rather than rely on their existing labor models. And if they hesitate, Sikka’s new wave of AI-native IT services will be waiting to pull their clients forward.
