AI startups with no revenue or products are using a legal but controversial funding tactic to supersize their valuations by tens of millions of dollars in just weeks. The strategy involves splitting funding rounds into two tranches at wildly different prices, allowing early investors like Sequoia to buy in at a low valuation, then sell their stake at a dramatically higher price when later-stage investors jump in. The result: headline-grabbing billion-dollar valuations that don't reflect the company's actual worth.

What Exactly Is a Tranched Funding Round?

A tranched round is when a startup raises money in two separate closings at different valuations. In the first tranche, an early investor like Sequoia might value a company at $55 million pre-money. Within weeks or even days, a second group of investors values the same company at $4 billion pre-money. Both groups own equity in the identical company, but they paid vastly different prices.

The most dramatic example is Ineffable Intelligence, a startup founded by David Silver, a renowned former scientist at Google DeepMind. Silver pitched his vision for AI systems that could learn without human data, eventually powering everything from toasters to other devices. Despite having no near-term product plans or revenue, Ineffable raised $11 million from Sequoia and others in the first tranche at a $55 million pre-money valuation. Within about a month, the company closed a second tranche of $1.1 billion at a $4 billion pre-money valuation from investors including Lightspeed, Index Ventures, and DST Global. That's a 70-fold increase in price for the same company in just weeks.

Why Are VCs and Founders Using This Strategy?

For startups, the benefits are clear. A billion-dollar headline attracts top talent, who often base salary decisions on a company's valuation and equity package. It also helps with future fundraising rounds and business development deals. For early investors like Sequoia, the tactic offers a chance to back star founders at a lower price, securing more ownership and the potential for massive returns if the company succeeds.

The practice has become especially common among "neolabs," companies that raise billions right out of the gate to focus on frontier AI research rather than building products. These companies need enormous amounts of capital upfront to purchase graphics processing units (GPUs), the specialized chips required for training large AI models. Because the capital requirements are so steep, lead investors wanted better terms in return, according to venture capitalists familiar with the deals.

"At some point there'll be some pushback from VCs, but the market's so hot right now that you have no other choice really if you want to get exposure to some of the best companies," said Zach DeWitt, a partner at Wing VC.

Zach DeWitt, Partner at Wing VC

The scale of this trend is staggering. Over 63 neolabs were collectively valued at more than $300 billion and raised about $48 billion, according to data from Menlo Ventures partner Deedy Das. That $48 billion accounts for roughly 16 percent of the $283 billion invested in startups other than OpenAI or Anthropic over the last year.

How Does This Distort the Market?

The problem is that the public only hears about the higher valuation. When Ineffable Intelligence announced its $1.1 billion raise, headlines focused on the $5.1 billion post-money valuation. Few outlets reported that Sequoia had invested most of its capital at a much lower price, or that the company's actual blended valuation, a weighted average of both tranches, was far lower. This creates a misleading impression that blue-chip firms like Sequoia have extreme conviction in the startup.

"In a market where fundraising runs on vibes, a billion-dollar headline is worth a lot more than an accurate one," said Jaya Gupta, a partner at Foundation Capital.

Jaya Gupta, Partner at Foundation Capital

The tactic has raised concerns about transparency and founder honesty. Brendan Foody, CEO of Mercor, posted on X in early June that he had seen half a dozen rounds where Sequoia invested in two tranches, calling it the "Sequoia Scam." Foody noted that founders often misrepresent the higher valuation to employees and pitch it to angel investors without disclosing the lower earlier price. He later acknowledged that the practice is common across all top venture firms, not just Sequoia.

Sequoia partner Shaun Maguire responded to the criticism, noting that it has happened only five times during his seven-year tenure at the firm. He explained that the higher valuations typically come from other investors willing to pay premium prices for hot AI companies, not from Sequoia's own conviction. "What happens is other investors are willing to pay a high price for a hot company, usually AI," Maguire wrote. "That's multiples above what we're willing to pay".

How to Evaluate AI Startup Valuations Accurately

• Ask for the blended valuation: Request the weighted average of all tranches, which represents the company's actual value based on the total capital raised and equity given up. This number is rarely reported publicly but is the most accurate measure of what investors collectively believe the company is worth.
• Examine the timing and size of each tranche: Look at how much capital was raised in each round and how far apart the closings were. A massive jump in valuation over a short period, especially when the company has no revenue or product, is a red flag that later investors may be overpaying.
• Check who invested at each price: Early investors like Sequoia typically invest at lower valuations. If later-stage investors from corporate venture arms (like Google or Microsoft) are paying much higher prices, they may be less price-sensitive because they expect future business deals rather than pure financial returns.
• Look for product milestones and revenue: Companies with actual products, users, or revenue should have valuations tied to those metrics. Startups with only a concept or pitch should command much lower valuations, regardless of the founder's pedigree.

The practice also benefits later-stage investors who missed out on earlier AI successes like OpenAI and Anthropic. These funds see neolabs as a way to place bets on the next wave of cutting-edge startups in a highly competitive market where access to the best deals is limited. Even if the startup's business prospects are unclear, the sheer demand from investors creates opportunities to resell stakes at higher prices.

The trend reflects a broader shift in how AI funding works. Lead investors now actively encourage startups to raise second tranches simultaneously, using their brand name and reputation to drum up interest and create competition among later-stage investors. This urgency and competition can drive valuations far beyond what the company's fundamentals would suggest.

While tranched rounds are not illegal or inherently unethical, they highlight a tension in the current AI funding market. Founders and early investors benefit from inflated headlines, while employees, later-stage investors, and the broader market may be making decisions based on misleading valuation figures. As the AI funding frenzy continues, transparency about how valuations are calculated and what they actually represent will become increasingly important for all stakeholders.