
Image: Flickr / Wikimedia Commons
Anthropic and OpenAI Are Both Building Enterprise AI Consulting Arms
Two rival AI labs, one week, near-identical playbooks: pair frontier models with Wall Street capital and embed engineers directly inside companies.
This article was produced by the AETW editorial team.
Anthropic has launched a $1.5 billion enterprise AI joint venture with Blackstone, Goldman Sachs, and Hellman & Friedman - just hours after OpenAI announced a near-identical $10 billion structure. Both are betting that the real enterprise AI revenue model looks less like software and more like implementation consulting.
Two announcements, one day apart
On May 4, 2026, Anthropic announced a new AI-native enterprise services firm formed alongside Blackstone, Hellman & Friedman, and Goldman Sachs. The venture is backed by $1.5 billion in committed capital, with Anthropic, Blackstone, and Hellman & Friedman each contributing roughly $300 million, and Goldman Sachs adding approximately $150 million. Additional backing comes from Apollo Global Management, General Atlantic, Singapore's sovereign wealth fund GIC, Leonard Green, and Sequoia Capital.
Hours before Anthropic's announcement, Bloomberg reported that OpenAI had finalized a near-identical structure called The Development Company - a $10 billion venture backed by 19 investors including TPG, Brookfield Asset Management, Advent, and Bain Capital. The two ventures share no overlapping investors, but they share the same underlying thesis: that deploying frontier AI at enterprise scale requires more than a model and an API key.
The Palantir model, scaled
Both ventures are explicitly modeled on Palantir's forward-deployed engineer (FDE) approach - where engineers embed inside client organizations to redesign workflows around the technology, rather than handing over a product and walking away. Anthropic's announcement describes engagements where its engineering team sits down with clinicians and IT staff to build tools that fit into workflows staff already use.
Blackstone President Jon Gray framed the problem plainly: one of the most significant bottlenecks to enterprise AI adoption is the scarcity of engineers who can implement frontier AI systems at speed. Goldman Sachs' Marc Nachmann added that the venture would help democratize access to forward-deployed engineers for companies that currently can't afford the talent - or the consulting fees - to build AI systems on their own.
The practical implication is that these ventures are positioning directly against McKinsey, Accenture, and other major consulting firms. For every dollar companies spend on software, they spend roughly six on services - a ratio that has made consulting a multitrillion-dollar industry. Both Anthropic and OpenAI are now betting they can capture a meaningful share of that spend by pairing model access with implementation capability.
Why Wall Street, why now
The choice of private equity and alternative asset managers as founding partners isn't incidental. Firms like Blackstone, Apollo, and General Atlantic collectively manage portfolios spanning hundreds of mid-sized companies - exactly the segment both ventures are targeting. The structure effectively gives Anthropic and OpenAI a built-in client pipeline: investors have financial incentive to push adoption across their portfolio companies, and the AI labs get repeatable deployment channels without building a traditional enterprise sales force from scratch.
This also coincides with a critical window for both companies. Anthropic's annualized revenue recently surpassed $30 billion, more than tripling from $9 billion the prior year, and the company is reportedly in late stages of a funding round targeting a $900 billion valuation. OpenAI closed $122 billion in funding in late March against an $852 billion valuation. Both are eyeing public markets as early as this year. Enterprise revenue depth - not just model subscriptions - will matter significantly for those IPO narratives.
The open question
The structure raises a real tension that neither company has fully addressed. If Anthropic and OpenAI are both embedding engineers inside portfolio companies and redesigning core workflows, they are taking on the organizational complexity, accountability, and timeline risk that consulting firms have historically managed. Software companies that try to become services businesses often find the economics messier than expected - slower to scale, harder to staff, and margin-compressing in ways that don't show up in a press release.
There is also the question of model lock-in. Investors committing $300 million alongside Anthropic are not neutral arbiters recommending the best tool for each engagement - they are structurally incentivized to deploy Claude. Whether enterprise customers view that as a feature or a conflict will vary, but it is worth watching how the market responds once the first real engagements go public.
What to watch
- Neither venture has been publicly named yet - branding and structure details are still pending.
- OpenAI's Development Company is operating at a significantly larger scale ($10B vs $1.5B) and with a broader investor base (19 firms vs roughly 8).
- Anthropic's $900 billion valuation round is reportedly in final stages - this joint venture announcement likely supports that raise.
- The target market is mid-sized companies inside PE portfolios, not the Fortune 500 - a segment that has historically been underserved by both enterprise software and top-tier consulting.
- Both Anthropic and OpenAI are eyeing IPOs as early as 2026. Enterprise revenue velocity will be a key metric for public market investors.
Sources
AI & Technology Researcher
Brian Weerasinhe is the founder and editor of AI Eating The World, where he covers artificial intelligence, tech companies, layoffs, startups, and the future of work. His reporting focuses on how AI is transforming businesses, products, and the global workforce. He writes about major developments across the AI industry, from enterprise adoption and funding trends to the real-world impact of automation and emerging technologies.


