Which startups have raised the most funding in the AI lab market?

A constantly refreshed list of the top startups in the AI lab market, ranked by cumulative funding raised

A quick summary table

Metric	Value
Most funded startup	OpenAI, with $59.9B raised
Second most funded startup	Anthropic, with $58.7B raised
Largest funding round	OpenAI’s $40.0B growth round in March 2025
Median funding	About $303.5M across the ranked AI lab startups
Share of funding captured by the top 10	About 81% of total funding in this ranking
Median time since last round	About 16 months
Startups that raised funding in the last 12 months	42 out of 98 startups
Frontier AI lab funding concentration	OpenAI, Anthropic, and xAI raised about $155.6B combined
AI compute infrastructure funding	Cerebras, CoreWeave, Groq, Crusoe, and Lambda raised about $20.7B combined
Investor focus across the AI lab stack	Capital is concentrated in models, compute, inference, chips, and evaluation infrastructure

Top startups in the AI lab market ranked by total funding raised

Here is an updated table that ranks the top startups in the AI lab market based on the total amount of funding they have raised to date.

The table also includes the total number of funding rounds, the date and size of the latest round, the financing type (e.g. Series A, equity financing), key investors, the startup’s current status (active, IPO, acquired, or shut down), and a confidence score based on the data collected (we excluded startups with very low data confidence, to make sure everything is reliable).

Key funding trends in the AI lab market

A few word about our methodology

As you can see, we built a database that ranks startups in the AI lab market based on their total cumulative fundraising. To create this ranking, we reviewed many sources and cross-checked information across multiple places.

Whenever possible, we prioritized official company communications, since official announcements are usually the most reliable source for funding amounts in the AI lab market. When those were not available, we relied on reputable industry sources such as TechCrunch, Crunchbase, Financial Times or Forbes (to name a few).

We excluded random blogs, unverified websites, and any sources that could not be validated.

When funding rounds were announced in other currencies such as euros, British pounds, Chinese yuan, Swiss francs, Singapore dollars, Australian dollars, or rupees, we converted them into approximate USD equivalents for consistency.

Sometimes different sources report slightly different numbers, or the exact round size is not fully disclosed. This is especially common in the AI lab market, where strategic rounds, public listings, secondary transactions, and infrastructure financing can be hard to separate. In those cases, we flag the uncertainty and assign a confidence label to each startup, visible in the last column.

Here is what they mean.

Full confidence: The company’s equity fundraising history can be reconstructed completely from public sources. The rounds, dates, amounts, and key investors are clearly identified, with no meaningful gaps.

Strong confidence: The fundraising history is largely complete and reliable. There may be a small missing detail, such as incomplete investor information or a minor round with limited data, but the overall record is clear.

Partial confidence: The main fundraising rounds can be identified, but the record is incomplete or somewhat mixed. Some rounds may be missing or certain funding events may be difficult to separate clearly.

Low confidence: Public information is too limited, inconsistent, or ambiguous to reliably reconstruct the company’s equity fundraising history.

When the confidence level is too low, we take a conservative approach and exclude the company from the ranking. We don’t want to include data that cannot be reliably verified.

This reflects how we conduct all our research on the AI lab market.

In a world where LLMs hallucinate and unreliable information is everywhere, our goal is simple: provide data you can trust.

If you want the full detail on a specific calculation, feel free to contact us and we will gladly explain.

Finally, know that we update the dataset once per month, so come back here if you need fresh information.

Who is the author of this content?

NEW MARKET PITCH TEAM

We track new markets so founders and investors can move faster

We build living “market pitch” documents for emerging markets: from AI to synthetic biology and new proteins. Instead of digging through outdated PDFs, random blog posts, and hallucinated LLM answers, our clients get a clean, visual, always-updated view of what’s really happening. We map the key players, deals, regulations, metrics and signals that matter so you can decide faster whether a market is worth your time. Want to know more? Check out our about page. You can also follow us on Instagram or on Facebook.

How we created this content 🔎📝

At New Market Pitch, we kept seeing the same problem: when you look at a new market, the data is either missing, paywalled, or buried in 300-page reports that feel like they were written in the 80s. On the other side, LLMs and random blog posts give you confident answers with no sources, and sometimes they just make things up. That’s not good enough when you’re about to invest real money or launch a company.

So we decided to fix the experience. For each market we cover, we build a structured database and update it on a regular basis. We track funding rounds, fund memos, M&A moves, partnerships, new products, policy changes, and the real activity of startups and incumbents. Then we turn all of that into a clear “market pitch” that shows where the opportunities are and how people actually win in that space.

Every key data point is checked, sourced, and put back into context by our team. That’s how we can give you both speed and reliability: fast coverage of new markets, without the usual guesswork.