One of the most well-known names in enterprise software is making a pointed bet: the traditional IT services model is dying, and he wants to be the one to replace it.

Vishal Sikka, former CEO of Infosys and former CTO of SAP, launched Hang Ten Systems this week with a $32 million seed round led by Mayfield, with a strategic investment from Aramco Ventures. The startup is positioning itself as an AI-native alternative to the consulting and IT outsourcing model that companies like Infosys, TCS, and Accenture have built their empires on.

“It is time to ride this wave,” Sikka said of the AI moment, in an apparent nod to the company’s surfing-inspired name.

What Hang Ten Actually Does

Hang Ten Systems describes itself as an AI-native enterprise services company built around three things: agentic code generation, reusable AI skills, and deep domain expertise.

In practice, that means the company helps enterprises continuously build, modify, and operate software using AI-driven development and automation — without the massive consultant headcounts that traditional IT engagements require.

The co-founding team has serious depth. Navin Budhiraja serves as CTO, Sanjay Rajagopalan as chief design officer, and Tao Liu as senior vice president of forward deployed engineering. The board includes Yahoo co-founder Jerry Yang.

Early customers include Siemens Gamesa Renewable Energy and Fresenius, which gives the company immediate credibility in industrial and healthcare sectors — two verticals where custom software development has historically required expensive, long-cycle IT projects.

The Thesis That Puts the Big Players on Notice

The economic model of traditional IT services is simple: you sell time. More projects mean more consultants. Revenue scales with headcount.

Mayfield backed Hang Ten specifically because it believes this model is breaking. The firm’s thesis, as stated directly: AI-native firms can build leverage across every project they complete, so their capacity grows with learning, not hiring.

That’s a fundamentally different unit economics story. And if it plays out at scale, it rewrites the P&L of every firm currently charging clients by the billable hour.

Sikka’s Infosys tenure (2014-2017) was defined by his attempts to push the company toward AI and automation — a direction that repeatedly ran into internal resistance. Hang Ten reads, in some ways, like what he was trying to build from the inside out.

Why This Is Happening Now

The timing matters. Every major IT services firm is currently scrambling to reposition around AI, striking partnerships with Anthropic, OpenAI, and Microsoft to prove their relevance. But bolting AI onto a fundamentally headcount-based delivery model is a structural fix, not a transformation.

That gap is exactly what Hang Ten is targeting. By building AI-first from day one, the company can price and deliver differently from the start.

This mirrors what’s happening across the business services landscape more broadly. Companies are not just asking which AI tools to license — they’re questioning whether the people and processes they used to outsource actually need to be that expensive anymore.

What This Means for Business

If you’re a business owner or operations leader, the Hang Ten story is a signal worth paying attention to for two reasons.

First, the cost of custom software development and IT services is about to get structurally cheaper. AI-native service firms will compete on outcomes and speed, not the size of their consultant bench. That means leverage you didn’t have before.

Second, the “build vs. outsource” question now has a third answer: build with AI-native partners who can move at software speed without consulting firm pricing.

This isn’t a distant trend. Siemens Gamesa and Fresenius are already doing this. The companies that figure out how to work with AI-native vendors in the next 12-18 months will end up with better software, faster, for less money.

The real risk isn’t adopting AI. It’s continuing to buy services built on the old model while your competitors aren’t.

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