Anthropic and Blackstone have launched Ode, a $1.5bn joint venture dedicated to installing AI systems in businesses rather than training models.
The shift from models to deployment
While AI capabilities continue to improve, the question of how enterprises will actually use that technology remains unanswered. Labs like Anthropic and OpenAI have responded by creating separate units to place engineers directly into customer offices. This strategy rests on the belief that helping businesses apply their own models represents the next trillion-dollar market.
Ode is the result of a collaboration between Anthropic and a group including Blackstone, Hellman & Friedman, and Goldman Sachs. The venture began in May. It mirrors OpenAI’s own move to launch The Deployment Company, highlighting a growing realisation that better software alone does not win enterprise contracts.
The idea originated with Blackstone. When the private equity firm tried to implement AI across its portfolio using large consultancies and small specialist shops, results were mixed. One small startup, Fractional AI, stood out. Blackstone acquired it shortly after the joint venture was announced. Fractional had previously worked with OpenAI for 11 months before that partnership ended.
Fractional now forms the base of Ode, operating as a scaled boutique firm. Its leadership has set ambitious targets.
“It’s pretty easy to imagine this as a trillion-dollar company someday if we execute well,” Chris Taylor, CEO of Ode and co-founder of Fractional, told TechCrunch. “The key challenge of the business is how do you go through that phase of hyper growth without losing the emphasis on quality?”
The company currently employs 100 engineers. It works with Anthropic’s applied AI team to find opportunities for the technology within various businesses and builds custom systems for each organisation’s operations. Anthropic’s internal staff will continue to handle strategic, mission-aligned projects, according to a spokesperson. The private equity backers will direct their own portfolio companies to Ode as potential clients, though the firm will sell to others outside that group.
Taylor says the ideal client is one whose chief executive accepts the value proposition.
“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 follows a “Claude-first” principle. It will implement Anthropic’s technology, such as Claude Tag in Slack, whenever possible. The firm is not restricted to Anthropic’s tools, however, and will use competitor products if required.
Eddie Siegel, Ode’s chief technologist and a co-founder of Fractional, argues the venture’s advantage lies in implementation quality and the ability to build custom solutions for specific 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 is that non-AI companies will be among the biggest winners if they adopt the technology correctly. Rewiring core business processes or customer experiences with AI requires significant assistance, he said.
“That requires top-caliber applied AI talent, which is not something most companies have,” Taylor said.
Executives describe the team as elite generalist software engineers. Over half are former founders. Siegel notes they can handle challenging technical problems while owning the project from start to finish. A Blackstone executive described them as “grown-up” engineers, the “special forces” rather than an army of forward-deployed engineers.
People involved in the venture told TechCrunch that demand for these teams far outstrips supply. Ode aims to scale internationally while keeping its boutique positioning. This involves running constant evaluations to measure the business impact of every AI implementation.
Maintaining such a team is difficult when top engineering talent is already scarce. If becoming an elite applied AI engineer requires experience as an entrepreneur, systems-first thinking, technical skills, and enterprise product judgement, can Ode train enough people to meet demand?
This challenge is compounded by competition. Ode faces OpenAI’s The Deployment Company as well as consulting giants like Deloitte and Accenture, which have formed their own forward-deployed engineer teams.
Siegel is not too worried about a shrinking pool of 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 right, the next great AI race will not just be about the best models, but about who can successfully put those models to work inside the world’s largest companies.
