A growing number of artificial intelligence startups are selling identical equity shares at significantly different prices within the same funding rounds, a practice that allows them to claim inflated valuations whilst offering select investors more favourable terms, according to multiple reports from TechCrunch AI and industry analysts.
The dual-pricing structure typically involves offering common shares to employees and early backers at one price, whilst selling preferred shares to new investors at a substantially higher valuation—sometimes double or triple the lower tier. This creates a blended valuation that can push companies past the $1 billion unicorn threshold on paper, even when the actual investment terms suggest a more modest worth.
Mechanics of the Practice
According to TechCrunch AI’s reporting, the arrangement works by establishing two distinct share classes within a single funding event. Institutional investors receive preferred equity with enhanced rights and protections at an elevated price point, whilst existing shareholders and employees hold common stock valued at the lower tier. When companies announce their funding rounds, they typically cite the higher valuation figure.
The practice has gained traction as competition for AI investment intensifies. With capital flooding into the sector—global AI startup funding reached $75 billion in 2025 according to industry trackers—founders face mounting pressure to demonstrate escalating valuations that justify continued investment and attract top talent.
Market Implications
The dual-price approach creates asymmetric outcomes across stakeholder groups. Founders and venture capital firms benefit from headline valuations that enhance their market positioning and facilitate subsequent fundraising at higher prices. Late-stage investors gain downside protection through preferential terms that aren’t reflected in the public valuation.
However, employees holding stock options face potential dilution and confusion about their equity’s true worth. If a company exits below its stated valuation, preference stacks ensure institutional investors recover their capital first, potentially leaving common shareholders with minimal returns despite the unicorn designation.
The practice also distorts market signals that other startups, investors, and analysts rely upon for benchmarking and decision-making. When valuations become detached from underlying economics, capital allocation grows less efficient, potentially inflating bubble dynamics within the AI sector.
Regulatory and Transparency Questions
Unlike public companies subject to rigorous disclosure requirements, private startups face minimal obligations to reveal their capital structures. This opacity enables dual-pricing arrangements to proliferate without external scrutiny. Securities regulators have not issued specific guidance on the practice, though some legal experts suggest it may warrant examination if it materially misleads stakeholders.
Several prominent venture capitalists have begun questioning whether the practice represents sound long-term strategy. Critics argue that manufactured valuations create expectations companies cannot meet, leading to painful down rounds when reality asserts itself. Supporters counter that sophisticated investors understand cap table complexity and that dual-pricing simply reflects different risk profiles.
Historical Precedent
The approach recalls tactics from previous technology bubbles, particularly the late 1990s dot-com era when companies employed creative accounting to justify unsustainable valuations. The 2021 special purpose acquisition company boom similarly featured structures that obscured true valuations until post-merger reality emerged.
What distinguishes the current AI wave is the sheer concentration of capital and the sector’s strategic importance, which may insulate companies from normal market discipline longer than previous cycles.
What Comes Next
Industry observers are monitoring whether dual-price structures become standard practice or face pushback from limited partners demanding greater transparency from their venture managers. The first major AI unicorn to exit below its stated valuation will provide a crucial test case for how these complex structures perform under stress.
Employees considering offers from AI startups should scrutinise not just headline valuations but the preference stack and liquidation terms that determine what their equity is actually worth. As one venture source told TechCrunch: understanding the cap table matters more than the valuation number.
The dual-pricing trend underscores how AI’s extraordinary growth has strained traditional venture capital frameworks, creating novel structures that may reshape startup financing—or simply repeat familiar mistakes with new terminology.
