AI Bubble: Circular Financing Risks in 2026

The current AI frenzy is a double-edged sword for everyone in the game, from investors to marketers, and one of the sharpest edges is circular financing. It’s a simple enough concept: money flows between companies that are all connected, puffing up valuations without any real customers showing up. This is how you get an AI bubble, and when it bursts, it’s going to take a big chunk of the tech sector with it. At this point, we know some AI valuations are a joke. The real job is figuring out how to tell sustainable growth from speculative froth.

The biggest headache for anyone in the market right now is telling the difference between a legitimate, value-driven AI investment and a house of cards built on circular financing. This lack of clarity leads to bad bets, overpriced assets, and eventually, a market correction that’s going to hurt. The dynamic is subtle. A VC invests in an AI startup. That startup then uses the cash to buy services from another company in that same VC’s portfolio. Sometimes this is just smart teamwork. The line gets blurry, though, when these inside deals become the main driver of growth metrics and valuation bumps, replacing genuine sales to outside customers.

Just look at the trajectory of some AI infrastructure providers lately. Their valuations have exploded, and while you’d think it’s from a wave of new, independent enterprise clients, it’s often from a series of strategic investments and subsequent purchases from other startups in the same VC’s family. It creates a powerful illusion of rapid growth. For example, a company announces a Series B with a monster valuation increase, but if you look closer, you find a huge piece of the investment came from a fund that also has a big stake in a “partner” company that, overnight, became the AI startup’s largest “customer.” When this kind of internal trading isn’t backed by external demand, you’re looking at a house of cards. The problem gets worse when those inflated valuations are used as collateral for loans or as benchmarks for the next funding round, sucking in outside investors who don’t know the full story.

So how did we get here? Early on, AI investment was all about a founder’s “vision” or the coolness of the tech, completely ignoring fundamental business sense. Investors got swept up in the hype around new AI capabilities and forgot to apply the same financial rigor they’d use for any other industry. We saw a gold rush to fund anything with “AI” in the pitch deck, often with no clear path to making money. A perfect example was the boom of AI-powered “personal assistants” in the early 2020s. They raised a ton of seed money on slick demos but had no compelling use case. They struggled to find real users and ended up surviving on more internal funding rounds or getting acquired by a bigger, often affiliated, tech company, instead of ever turning a profit. That first wave of inflated valuations set a bad precedent for the more sophisticated circular financing schemes we see now.

Fixing this requires a two-front war: investors need to get way more diligent, and AI companies need to adopt transparent reporting standards. First, investors must perform enhanced due diligence on funding sources and customer acquisition. Your analysis needs to go past the P&L to demand detailed breakdowns of revenue streams. You have to separate revenue from real, arm’s-length clients from revenue generated by companies within the same investment family. Ask for the client roster and cross-reference it with the lead investors’ portfolios. If a big chunk of their revenue or projected growth is from “strategic partners” who happen to share the same investors, that’s a red flag that needs a serious look.

Second, you have to obsess over verifiable metrics of market adoption and customer churn. How many unique, paying customers does the product actually have outside of the investor’s immediate network? What’s the average contract value, and (this is the big one) what’s their retention rate? A high churn rate is a killer, even if they’re signing new accounts, because it means the product isn’t delivering long-term value. You should be looking for a low customer acquisition cost (CAC) and a high customer lifetime value (LTV) from a wide range of customers. As a late 2025 eMarketer report pointed out, investors are finally shifting their focus to profitability and sustainable growth in the AI space, which is a direct reaction to the valuation madness of the last few years.

Third, demand transparency in related-party transactions. Companies should have to clearly disclose any major deals with businesses where there’s common ownership. A pattern of these transactions that conveniently props up revenue or valuation numbers needs to be scrutinized. The point is to make sure strategic partnerships are adding real value in the open market, not just serving as internal accounting tricks. I’ve personally seen startups where the “impressive” revenue growth was almost entirely from a single contract with a sister company, making their actual market validation pretty much zero.

Fourth, you have to diversify investment strategies beyond early-stage “unicorn” chasing. I get it, the allure of a massive return from a brand-new AI startup is powerful, but a balanced portfolio needs to include more established AI firms that have already demonstrated consistent, profitable growth. These companies typically have a diverse client base and a much clearer business model, mitigating the bubble risks that come with speculative early-stage ventures. Look at publicly traded companies with a strong AI component, or private equity investments in mature AI solution providers. The market for AI is growing up, and the best opportunities aren’t all at the seed and Series A rounds anymore.

Fifth, prioritize AI solutions that solve tangible business problems with clear, measurable ROI. The “coolness factor” of a new algorithm needs to take a backseat to its ability to deliver quantifiable economic benefits. Can you show me how it’s reducing operational costs by X percent? Or increasing conversion rates by Y points? Or automating a specific, expensive manual process? Answering those questions is what leads to sustainable customer relationships and real revenue. A Q1 2026 IAB report hammered this home, emphasizing the growing demand for strong ROI measurement to justify AI investments with concrete financial results.

Finally, you’ve just got to cultivate a healthy skepticism. If a deal seems too good to be true, especially in a hot segment like AI, it usually is. Applying a critical filter to these opportunities is just smart investing. Why is a company with minimal external revenue commanding a valuation like an established, profitable enterprise? You have to ask that question. The fear of missing out (FOMO) is a powerful force in investing, and it’s exactly what circular financing schemes are designed to exploit.

If we get more disciplined about AI investing and start calling out these circular financing games, the whole market gets healthier. Valuations will start to reflect economic reality and genuine market demand, not just internal money-shuffling. That discipline protects investors from the pain of an inevitable correction and channels capital toward AI companies that are actually building something useful. The companies with solid business models and real customers will find it easier to get funding, while those built on inside deals will either have to adapt or die. That’s how we build a durable AI industry: by rewarding real-world impact and sustainable business practices.