• OpenAI is now worth $157 billion, but it can’t rest easy yet.
• High costs, competition, and a lack of profitability remain challenges as it seeks to build more powerful AI models.
• Investors and founders must now consider how long this competition can last.

OpenAI just closed the most lucrative funding round in Silicon Valley history. Now comes the hard part: emerging victorious in a fiercely competitive AI industry.

Though Sam Altman’s company cemented its status as a frontrunner in the generative AI boom this week, securing a new $157 billion valuation after raising $6.6 billion of fresh capital from marquee investors, its leading position is hardly guaranteed.

Since OpenAI launched ChatGPT in late 2022, it has become clear that its mission to build large language models that rival human intelligence will involve exorbitant costs that require vast resources.

Sure, Altman’s company now casts a massive shadow over the industry with its fresh $157 billion valuation. That said, plenty of rivals are chasing him in an effort to compete for capital and resources, complicating the startup’s path to profitability.

So, while OpenAI has a moment to celebrate, we’re about to discover how deep its moat is and whether a brutal wave of consolidation is coming for Silicon Valley’s hottest industry.

An expensive race begins

While OpenAI’s new valuation and its fresh injection of capital represent huge sums that would be the envy of any founder in Silicon Valley, there are signs that Altman remains on edge.

According to a Financial Times report on the fundraising, Altman’s almost nine-year-old business asked its new backers — a lineup led by Thrive Capital and includes the likes of Nvidia, SoftBank, and Microsoft — to avoid investing in rival firms, of which there are plenty.

Anthropic and Mistral, both worth billions of dollars, are looking to take on OpenAI. So, too, are Elon Musk’s xAI and Safe Superintelligence (SSI), the startup launched in June by Ilya Sutskever, the former chief scientist at OpenAI who led a failed coup against his former boss.

“For the biggest model builders, these mega-rounds are the new normal, as the cost of training runs for the biggest models are climbing into the hundreds of millions of dollars,” said Nathan Benaich, founder and partner at Air Street Capital, an investment firm.

There are a few good reasons why OpenAI can’t rest easy.

For one, the costs of delivering game-changing generative AI advancements look set to spiral. Dario Amodei, CEO of Anthropic, said earlier this year that he expected training runs for AI models to top $10 billion by 2026 and rocket to $100 billion after that.

OpenAI’s own training costs could hit north of $3 billion a year, The Information previously estimated. GPT-4o costs around $100 million to train, but the cost will likely rise, depending on the complexity of future AI models.

In part, the costs are driven by purchases of powerful chips — known as GPUs — bought primarily from Jensen Huang’s Nvidia to build clusters in data centers. These are essential for providing the computing power needed to run LLMs.

The battle to retain talent has also been vicious in the current frenzy, with AI labs vying for an edge over competitors being pushed to offer increasingly eye-watering compensation packages.

“These costs will only grow as companies invest more and more to battle for often marginal performance advantages over their competitors. This race is without obvious historic parallels, thanks to the eye-watering capex demands and the lack of a clear path to profitability,” Benaich told BI.

Though OpenAI’s new arsenal of capital will help it fund some of the more expensive elements of its business, it’s not exactly operating from a position of strength just yet. Last week, a report from The New York Times revealed that the hottest AI lab on the planet was on track to end the year with a $5 billion loss.

And OpenAI’s rumored attempt to insist on exclusivity among investors could have drawbacks. Benaich described the move as “unusual” — but also a “perception of how it perceives its own market power.”

“It’s also a bold move that risks drawing unwelcome attention from competition authorities,” he added.

For industry experts, it raises questions about how sustainable this is for the industry in the long run.

Investors see consolidation on the horizon

As OpenAI moves to cement its position as the industry kingpin, investors anticipate some degree of consolidation among startups in the foundational model layer in the coming year.

LLM startups need constant access to a bevy of capital, but not everyone can tap into the same influx of cash as OpenAI. With Microsoft’s acquihire of Inflection.ai, and Google similarly absorbing Character.ai‘s founding team, investors expect more of these types of acquisitions in the near future.

“This is a capital race as well, and only investors such as sovereign wealth funds will ultimately be able to provide the types of capital needed for these LLM startups,” a European growth-stage VC told BI.

Once funding dries up, existing juggernauts, including the Big Tech giants, may scoop up smaller upstarts with a vertical focus. These companies have access to a wealth of proprietary data on which to train their models.

VCs also see a more sobering approach to backing LLM giants at lofty valuations. “A lot of other companies are raising capital on vision and hope, and I think that you’re going to start to see some rationalization around that,” another growth-stage VC told BI, adding that there would be “a level of cooling down of the froth around AI next year.”

“You don’t need 50 foundational model companies — you end up with two or four, maybe,” he said.

He added that those companies that do survive will be the ones that tangibly serve consumers. “You might have Amazon, Anthropic, OpenAI, Meta, and Google, but I can’t imagine there will be many others.”