How to Raise Funding in the AI Era: 5 Things Investors Want to See

As the startup funding market shifts in this new AI era, Socheat Chhay, Managing Director of Sopra Steria Ventures, reveals what separates fundable AI startups from the noise.

The numbers are hard to ignore. In the last couple months, OpenAI raised $122b at a $852b valuation, Anthropic raised a $30b Series G, and France’s Mistral AI has built a $14b AI empire. Q1 2026 set a record with $300b invested across about 6,000 startups globally, and AI captured 80% of that total. Even at the earliest stages, the capital flooding into AI is unprecedented, with former DeepMind researchers announcing the largest ever Seed round in Europe in April this year, raising $1.1b for their startup Ineffable Intelligence.   

Behind these headlines, business investors are raising the bar. Just two years ago, investors were backing AI companies on the strength of a team and a thesis. Now, with so much capital chasing a crowded market, the companies that win need proprietary data, domain depth, and team expertise to be defensible.

Socheat Chhay has seen this change up close. As Managing Director of Sopra Steria Ventures, the corporate venture arm of one of Europe’s largest IT services groups, he knows what it takes to raise funds in this environment. Sopra Steria backs startups from Seed to Series B, with a sharp focus on AI and deep tech with real enterprise deployment capacity.  

Tech Nation caught up with Socheat to find out what serious investors are actually looking for right now. 

1. Know Which Type of AI Company You Are

Not all AI business investment is the same, and the way investors evaluate your company depends heavily on where you sit.

“We separate AI into two main types,” Socheat says. “One is very horizontal: foundational models and infrastructure, the OpenAIs, the Anthropics. These are the largest deals. Then there’s verticalised applied AI, which is AI for specific business use.”

For horizontal infrastructure, investors are primarily backing a team’s ability to move fast and capture as many users as possible before a competitor does. Raise big, grow fast, and keep customers from leaving.

Verticalised applied AI, where most European founders are competing, works differently. Investors here are looking for depth, defensibility, and a clear understanding of the problem you’re solving better than anyone else. The expectations are higher and the scrutiny is sharper.

2. Always Prove Your Data Moat to Business Investors

Socheat keeps coming back to the importance of building proprietary data, algorithms and technology when looking to raise capital.

“We are looking at AI startups that are building proper expertise data which will stick with users ,” he says. “It’s not enough to build a vertical wrapper product on top of an existing model. The question is: do you have proprietary data which will produce outputs that nobody else can replicate?”

He points to an example of a well-funded and well-known firm which is built largely on top of a foundational model without a unique data and basic algorithm layer underneath. “They’re touching the surface of what legal contracting automation could be. But there’s no proprietary custom data. With a new free Claude legal plug-in released in the market, we don’t know what’s going to happen to them,” Socheat explains.

Before you raise funding, be ready to answer: what data do you own, how are you acquiring it, do they reinforce specialisation and why can a competitor not copy it? 

3. Don’t Just Sprinkle AI on a SaaS Product

One pitch pattern that immediately loses investor attention is founders who’ve bolted AI onto an existing product and are calling it an AI company.

“Every founder now sprinkles AI as a powder over their company to enter into AI investment thesis and gain valuation premium” he says. “At Sopra Steria we differentiate between a SaaS company augmented by AI, an AI wrapper and a company with a genuine AI-first moat.”

If you’re a traditional SaaS business venture with an AI layer or an AI wrapper, that’s not necessarily a problem. But the moat needs to come from somewhere else, deep workflow integration, switching costs, proprietary data, or a dominant position in a niche vertical. 

Founders who’ve thought carefully about the limits of AI, and positioned themselves accordingly, stand out.

4. Target the Right Verticals for Business Investment

Socheat is clear about where he’s putting his focus. He references a thesis from Sequoia’s Julien Bek that went viral on LinkedIn last month, It argues that the world’s next trillion-dollar company won’t sell software; it will sell an outcome, and use AI to deliver it. For example, instead of selling legal tech licenses per seat, autopilot agents backed by humans will output your contract reviews and due diligence. Open AI and Anthropic’s recent market notice to acquire an AI services company is confirming this structural trend.

That framing is central to how Socheat invests. “Anything that a services company does that can be replaced without human intelligence is the opportunity,” Socheat says. The sectors he’s watching: industry customer, manufacturing and supply chain operations, fintech infrastructure, orchestration middlewares, legal and compliance (KYC and AML) workflow automation applications, and devops engineering services.

“These are verticalised, highly replicable, and we’ve reached the stage where agents can replace human execution. That’s where we’re looking to put our needle,” he explains.

5. Understand the New Economics of Raising Capital

Socheat acknowledges a fundamental shift in how to raise funding in 2026. “A SaaS company used to need five to ten years to become a unicorn and still not be profitable at exit. Now we know that AI-native companies can get there in half the time, with a very limited team and be very profitable,” he says. “That changes everything about what we look for in a team, how valuations are made and how we think about capital.”

Founding teams look different too. Complementary or tech skills are commoditised and no longer enough. Investors want deep domain expertise combined with the ability to execute at even more velocity than before.

“The people building these companies need to be ultra-expert in their specific domain and be AI adopters,” Socheat says. “There’s much less time for the old iteration cycles. That model is gone.”

And because AI is replacing parts of your headcount, from tech to growth, you need less money than a late 2010’s Series B playbook would suggest. “We’re seeing founders requesting smaller rounds, because they’re replacing people who can have their tasks automated with tokens which outputs more than it could cost them in the future,” he says. “Investors are now looking at a new ratio: productivity of capital within a compressed timeframe.”

Show investors you understand this, he says. Know your new unit economics. Demonstrate how your raise reflects the AI-era cost structure, not the old one.

Click here to learn more about Sopra Steria Ventures.

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