Haiper Kills the Series A: Venture Capital Case Study
You play with fire, you get burnt.
If you are not familiar with acqui-hire and reverse acqui-hire, read my previous post and you will appreciate this one more. Remember, this is about and AI video start up dumpster fire but it could happen to any employee dependent start up.
When Haiper, a London-based AI video startup, launched in 2024, it seemed destined to become Europe’s breakout challenger in the generative AI video market. Founded by DeepMind veterans Yishu Miao and Ziyu Wang, the company promised to push beyond static AI imagery into fully coherent, dynamic video. With early demos showing rare temporal stability, Haiper raised a $13.8 million seed round led by Octopus Ventures and quickly positioned itself as a credible rival to Runway and OpenAI’s Sora.
For Seed and Series A venture capital investors, the thesis was straightforward: text-to-video was the next frontier in generative AI. The market for short-form video was exploding on platforms like TikTok, YouTube Shorts, and Instagram Reels. If Haiper could automate video creation at scale, the upside potential was enormous.
From day one, the founders were the company’s biggest asset. Their research pedigrees and product experience gave them credibility in the talent market and confidence with early-stage investors. The technology itself was promising, tackling the coherence issues that had plagued other AI video models. And the market opportunity was massive, with billions of hours of video being consumed daily across social platforms. For investors, the bet was simple: if text-to-image companies could attract sky-high valuations, then text-to-video represented the next generational wave.
By late 2024, Haiper had delivered enough to attract serious Series A attention. It had launched a consumer-facing AI video generation app, gained traction among creators, and built a recognizable brand in Europe. Internally, the team was making progress on improving video stability and scalability. For Series A investors, the path forward looked like a classic scale-up play: fund larger model training runs, expand cloud infrastructure, and move beyond consumer buzz into enterprise AI adoption for marketing, media production, and advertising.
But the red flags were hard to ignore. Training video models required enormous compute resources, and without discounted cloud credits from hyperscalers, the burn rate could spiral. The company was still heavily dependent on a small group of founders and senior engineers, raising concerns about talent concentration risk. And then there was the existential question facing every AI startup: would a hyperscaler like Microsoft, Google, or Meta actually buy the company, or would they just poach the talent when the time came?
In early 2025, that question was answered. Microsoft hired both of Haiper’s co-founders and several senior engineers. This wasn’t a traditional acquisition; it was a reverse acqui-hire. In this scenario, Big Tech absorbs the talent but leaves the corporate entity behind. The fallout was swift. Haiper shut down its consumer app in February. By June, NetMind acquired Haiper’s video-generation model and absorbed some of the remaining staff. What had looked like a potential European champion in AI video was effectively dismantled within months.
For investors, the implications were sobering. In a conventional acquisition, shareholders receive a payout, often at a healthy multiple. In a reverse acqui-hire, the most valuable resource, the talent, is absorbed by a hyperscaler, while the investors are left holding equity in a company that no longer has its core team. It’s a stark reminder of how fragile early-stage bets can be in the world of frontier AI startups.
Still, the decision to invest in Haiper was not irrational. The market opportunity was real, the product was validated by early users, and the founders were exceptional. This was exactly the type of asymmetric bet venture portfolios are designed to make. But Haiper’s collapse shows how venture capital risk management must adapt to the new dynamics of AI investing.
The first lesson is that reverse acqui-hires are no longer rare events, they’re a base case scenario. Investors in Seed and Series A rounds must assume there’s a real chance that their founders will be hired away before the company reaches maturity. That means deal structures need to evolve, with stronger key-person clauses, founder retention mechanisms, and downside triggers that protect investors if the core team leaves for a strategic.
The second lesson is that enterprise revenue is critical. Consumer traction might create buzz, but it doesn’t provide the financial ballast to protect against talent absorption. Enterprise contracts, even small ones, create recurring revenue, credibility with buyers, and stronger negotiating power with potential acquirers. More importantly, they help founders stay committed to building a business rather than treating the startup as a stepping stone to Big Tech.
Finally, Haiper forces investors to rethink exit strategies in AI startups. For decades, venture outcomes revolved around IPOs or acquisitions. In today’s AI market, a third outcome has emerged: talent absorption combined with partial asset sales. It doesn’t always wipe out investors, but it rarely delivers the venture-scale returns modeled in early funding rounds. A realistic venture playbook in 2025 must acknowledge this distribution of outcomes.
When your startup investment is floundering, no one is pouching your talent, so say good by to the 10x you need for overall return; your returns are marginal at best and complete losses at worse.
So what does Haiper’s story teach today’s generation of VCs? It’s not that AI startups aren’t worth backing, they are. The upside has to remain enormous or the industry is dead. Generative video, multimodal AI, and synthetic media will transform industries. But the risk calculus is different now. The gravitational pull of hyperscalers is stronger than ever, and hyperscaler talent absorption risk has to be built into every deal model.
The Haiper case is a reminder that Seed and Series A investors can do everything right, back exceptional founders, validate the product, and target a massive market, and still get burned when Big Tech swoops in. For venture capitalists, the solution isn’t to avoid AI to talent dependent start ups; it’s to adapt: structure deals with protection, diversify talent risk, push companies toward enterprise adoption earlier, and accept that reverse acqui-hire exits are part of the landscape.
Haiper wasn’t a bad bet. It was a rational one that collided with the realities of 2025 AI venture capital trends. For young investors entering the space, it’s a lesson worth internalizing. The biggest risk in frontier talent dependence isn’t competition or product failure; it’s that the founders you back today might be working for Microsoft tomorrow.
New structures like the VC Risk Swap have to be considered along with the innovation of talent contracts to protect the startup to get the 10x from the employee not share growth.