Artificial intelligence models are growing increasingly powerful, yet the precise shape of enterprise adoption remains uncertain. To help define that trajectory, labs such as Anthropic and OpenAI have established separate business units focused on deploying AI engineers directly into client offices—a bet that assisting organizations in harnessing these models represents the next trillion-dollar opportunity. One such venture now has a name: Ode with Anthropic, a $1.5 billion AI implementation company launched in May through a joint venture involving Blackstone, Hellman & Friedman, Goldman Sachs, and others. This follows OpenAI’s similar initiative, The Deployment Company, highlighting a growing recognition among frontier AI labs that winning enterprise clients demands far more than simply releasing better models.

Ode originally emerged from Blackstone’s observation of a gap: the firm had brought in both large consulting firms and small AI services boutiques to implement AI across its portfolio companies. One boutique, the AI engineering services startup Fractional AI, stood out, and the joint venture acquired it shortly after the announcement. (Fractional ended an 11-month partnership with OpenAI upon being acquired.) Fractional has become the foundation of Ode—essentially a “scaled boutique” AI services firm—and its leaders harbor ambitious plans. “The key challenge of the business is how do you go through that phase of hyper growth without losing the emphasis on quality,” one executive noted.

Currently, Ode employs 100 engineers and collaborates closely with Anthropic’s applied AI team to identify where the technology can impact various businesses, crafting systems tailored to each organization’s operations. The private equity firms backing Ode will direct their portfolio companies to the venture as potential customers, though Ode is not restricted to serving only those firms. For Ode, an ideal client is one whose CEO fully embraces the promise of AI, according to Taylor. “A lot of the work that we’re doing is the top one or two priority for the CEO of the company,” Taylor said. “It’s the most important product feature that the company is going to build over the course of the next two years, or it’s reworking the most important business process they have.”

Ode operates under a “Claude-first” principle, meaning it will implement Anthropic’s technology—including features like Claude Tag in Slack—whenever possible. However, the company is not limited to Anthropic’s offerings and will use rival AI products if necessary. Eddie Siegel, Ode’s chief technologist and a Fractional co-founder, describes the venture’s secret sauce as its implementation quality and ability to build custom solutions for business problems. “I think model selection matters, but it’s not where the majority of calories are spent,” Siegel said. “It’s one ingredient in a system that has to be engineered. It’s like the choice of programming language when you build a piece of software… I would not define an enterprise transformation in terms of whether they choose Python or Java.”

Taylor added that the founding belief behind Ode is that “non-AI companies are going to be among the big winners of this whole AI moment if they adopt the technology the right way.” However, taking AI—“this magic, hallucinating ingredient”—and rewiring core business processes or customer experiences with it requires substantial help. “That requires top-caliber applied AI talent, which is not something most companies have,” Taylor said. Ode’s executives describe their team as elite generalist software engineers, over half of whom are former founders—the kind of people who can “juggle a really challenging technical problem, but also own something end-to-end,” per Siegel. Or as one Blackstone executive put it: a team of “grown-up” engineers, the “special forces” rather than an army of forward-deployed engineers (FDEs).

Ode’s goal is to continue scaling, including internationally, while maintaining its boutique firm positioning—essentially running constant evaluations to measure the business impact of AI implementations. Yet in a world where top engineering talent is already scarce, maintaining and growing such a team presents a real challenge. If becoming an elite applied AI engineer requires entrepreneurial experience, systems-first thinking, AI expertise, and enterprise product judgment, can Ode train enough people to meet demand? These difficulties are compounded by competition not only from OpenAI’s The Deployment Company but also from consulting giants like Deloitte and Accenture, which have created their own FDE teams. Siegel isn’t overly concerned about a dwindling pool of grown-up generalist engineers. “It has never been an easier time to become an entrepreneur,” he said. “You learn so much by trying to own problems end-to-end, going to try and get product-market fit, move the needle on a business. You learn a lot there that you don’t learn from just solving a narrow problem. That’s the skill set that fits really well with Ode.”

Whether enough of those engineers will appear remains an open question. But if Ode and its backers are correct, the next great AI race won’t be solely about the best models—it will be about who can successfully put those models to work inside the world’s largest companies.