OpenAI has been central to one of the most dramatic surges in private-tech valuations in recent memory. In less than two years its ascent — from being a headline AI researcher to the centerpiece of a liquidity revolution — has helped pull up valuations across a group of top startups. Seven tech firms now stand as giants in the private market, collectively valued at about $1.3 trillion. This article explains in detail what OpenAI did to lead that surge, how that helped push up those seven startups, and what the implications are for the wider industry.

OpenAI’s strategy for igniting investor fervor

OpenAI’s rise has been powered by several reinforcing moves that convinced investors it was more than just a research lab. First: accelerating revenue growth. The company has scaled ChatGPT usage rapidly — hundreds of millions of users weekly — and expanded enterprise customers. That usage translated into subscription, API, and licensing income that doubled from early to mid-2025, pushing forecast revenue toward $20 billion by year-end. These are internal metrics that investors treat as signals that OpenAI can monetize scale, not just novelty.

Second, OpenAI opened up its models partially: releasing open-weight language models and making tools more broadly accessible. That gave developers, enterprises, and third parties more visibility into how its technology works, reducing perceived risk and giving confidence to capital providers that the models’ performance and roadmap are concrete, not opaque.

Third, OpenAI used secondary markets and employee share sales strategically. That means existing investors, founders, and staff got opportunities to sell shares at high valuations *before* any IPO. These moves provide liquidity and make the private valuation real and visible. When people outside can buy or see prices rising, it validates the valuation in ways that mere funding rounds often don’t.

Fourth, OpenAI is investing aggressively in infrastructure: datacentres, computing power, scalable model deployment. When investors see that a company is committing capital to hard assets and enduring cost bases (servers, accelerators, power, real-time inference capacity), it suggests long-term credibility and that scaling potential is real, not just speculative.

How that helped seven startups reach $1.3 trillion combined valuation

OpenAI didn’t act in isolation. Its dramatic valuation jump and liquidity moves lifted the entire field. Investors, seeing the strength of OpenAI’s monetization, began re-rating peer companies more generously. In particular:

• Anthropic: Following OpenAI’s template, this AI startup raised capital at high multiples, benefiting from the momentum that investors want exposure to large language models and foundation-model risk.
• xAI (Elon Musk’s AI firm): It too has seen valuation rises by tapping into the same investor fear of missing out, using secondary rounds or advanced funding rounds.
• Databricks: As a company already strong in data analytics, its investments in generative AI capabilities got revalued highly when investors saw infrastructure demand and usage surges in OpenAI.
• SpaceX, Stripe, Anduril, others: Some firms outside core LLM development also benefited from the halo effect, especially those with tech, data, hardware, or mission-critical operations. Investors extended higher valuation expectations across defense tech, fintech, and infrastructure companies tied to AI or scale.

In aggregate, these seven firms (OpenAI among them) have seen their valuations almost double in recent periods as investors priced future AI-driven growth strongly. OpenAI’s valuation talks (including secondary share sales imagining it at up to \~$500 billion) sharpened expectations across other startups that good growth + infrastructure + scale would be richly rewarded.

Industry implications of this private-market blastoff

This surge has multiple consequences for the broader tech and startup ecosystem.

• Liquidity before IPO: Many of these startups are staying private longer. Because private markets now offer meaningful liquidity via secondary share sales and advanced rounds, some companies are under less pressure to list publicly. That allows them to scale without quarter-by-quarter earnings pressure and avoid public-market volatility, at least in the near term.
• Valuation risk and bubble potential: While valuations have surged, so have risks. Some of the value assigned today depends on future performance: infrastructure scaling, model efficiency, regulatory acceptance, monetization of AI tools. If any of these underperform — for example, compute costs rise faster than revenue, or regulation limits certain AI deployment — there could be sharp valuation corrections.
• Increased competition for talent and infrastructure:The dominance of a few flagship AI firms means pressure on compute supply chains (GPUs, accelerators, cloud infrastructure), data centres, power, cooling, hardware engineers. Startups aiming to compete need to make deep capital commitments just to keep up.
• Shifting venture capital flows: More capital is channelled toward large, growth-stage AI companies and less toward small early-stage ideas unless they have very strong differentiation. VCs are increasingly investing in startups that show strong technical capabilities, infrastructure plans, and proof of user or revenue traction.
• Ripple effects in public markets and partners: Companies that build infrastructure, supply AI hardware, cloud providers, and adjacent platforms see benefits. When OpenAI’s contracts with major cloud providers are announced, or infrastructure demand goes up, those providers often benefit in share price or market interest, even though they are not directly private.
• Regulation, governance, and risk oversight: Rapid growth and large private valuations draw regulatory interest. Stakeholders (governments, investors, users) will push for transparency in AI safety, data privacy, fairness, model leaks, algorithmic governance. High valuation firms will face pressure to demonstrate not just growth but responsibility.

What to watch in the coming months

Key signposts will include how OpenAI handles its next funding or secondary sale rounds — i.e. the exact valuation achieved, how much liquidity is given to employees and early-investors, and whether any IPO plans surface. Also important is how the seven high-valued companies report their internal metrics: revenue growth, infrastructure costs, margins. If compute costs outstrip revenue, some valuations might hinge on optimistic projections rather than actual performance.

Another area is regulation and policy: how AI safety laws, antitrust scrutiny, export controls on advanced chips might affect ability to scale. Finally, whether the investor euphoria spreads too far, raising valuations of companies just tangentially connected to AI, which might set the stage for sharp corrections if expectations are unmet.

(Adapted from CNBC.com)