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Analysis

Anthropic Just Partnered With Goldman Sachs, Blackstone, and Apollo to Take on McKinsey. The AI Consulting Wars Have Begun.

This morning, Anthropic announced the formation of a new AI-native enterprise services firm alongside founding partners Blackstone, Hellman & Friedman, and Goldman Sachs β€” backed by a consortium that also includes Apollo Global Management, General Atlantic, Leonard Green,…

Market MunchiesΒ·May 5, 2026Β·11 min read
May 5 news2

This morning, Anthropic announced the formation of a new AI-native enterprise services firm alongside founding partners Blackstone, Hellman & Friedman, and Goldman Sachs β€” backed by a consortium that also includes Apollo Global Management, General Atlantic, Leonard Green, Singapore's sovereign wealth fund GIC, and Sequoia Capital. The total committed capital is $1.5 billion. The anchor partners β€” Anthropic, Blackstone, and Hellman & Friedman β€” are each contributing approximately $300 million. Goldman Sachs is in for $150 million.

The firm has not yet been named. What it is doing is clear enough: embedding Anthropic engineers directly inside mid-sized companies to redesign their workflows around Claude. Not licensing software. Not selling API access. Deploying engineers who stay, build, and maintain AI systems inside the client's own operations.

This is a direct attack on the consulting industry. And it is happening simultaneously with an almost identical move from Anthropic's primary rival.

What the Venture Actually Does

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The distinction between what this firm will do and what traditional enterprise software companies do is worth making precise, because it changes the competitive analysis entirely.

Conventional enterprise AI sales look like this: Anthropic sells an API license to a corporation. The corporation's IT team integrates it into their systems. The consulting firm β€” McKinsey, Bain, Accenture β€” charges separately to redesign the workflows that the AI is supposed to improve. The AI company captures the software value. The consulting firm captures the implementation value. The two are separate businesses with separate economics.

What this joint venture collapses those two into one. The new firm is a standalone entity with Anthropic engineering resources embedded directly within its team, a structure that mirrors Palantir's forward-deployment model and undercuts traditional consultants by combining implementation capability with ownership of the underlying model.

That last phrase is the critical distinction. Palantir deploys engineers inside clients, but Palantir does not own the foundation models it uses. McKinsey Digital has thousands of engineers, but McKinsey is licensing Claude, GPT-4, or Gemini β€” tools built by companies that are now directly competing with them for the same implementation contracts. Anthropic's engineers, by contrast, have direct access to the model's architecture, training pipeline, and product roadmap. They can customize behavior at the weights level, not just the prompt level. When an Anthropic engineer sits inside a hospital system redesigning a clinical workflow, they can modify the underlying model to fit the use case in ways that a McKinsey engineer using the same Claude API simply cannot. The ownership of the model is not just a cost advantage. It is a capability advantage that no consulting firm can close without building their own foundation model β€” which none of them are going to do.

The target customer is the mid-sized company β€” too large to manage AI deployment with internal IT resources, too small to afford McKinsey's rates for a multi-year transformation engagement. That market is enormous. For every dollar companies spend on software, they spend six on services β€” a ratio that has made consulting a multitrillion-dollar industry and that AI-native firms are now positioning to disrupt.

One caveat the announcement papers over: mid-sized companies are not blank slates. Most carry significant technical debt β€” legacy server infrastructure, siloed data systems, inconsistent record-keeping, and non-AI-ready workflows built over decades. The clean "workflow redesign" described in the press release is, in practice, a messy, manual slog through organizational inertia and data quality problems that no amount of engineering talent eliminates quickly. The venture's high-touch, labor-intensive model will scale more slowly than the $1.5 billion headline suggests, and its ability to serve hundreds of portfolio companies simultaneously is constrained by the fundamental scarcity of the engineers it is trying to deploy. That scarcity is the bottleneck the venture was formed to address β€” but it cannot be solved by press release.

Blackstone President Jon Gray identified the problem the venture is designed to solve: "One of the most significant bottlenecks to enterprise AI adoption" is the scarcity of engineers who can implement frontier AI systems at speed. Goldman's Marc Nachmann framed the financial logic: the venture will help "democratize access to forward-deployed engineers" for companies that currently can't afford the talent β€” or the consulting fees β€” to build AI systems on their own.

The Built-In Client Pipeline

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The most structurally important element of this deal is not the $1.5 billion in committed capital. It is the network of companies that capital represents.

Blackstone manages approximately $1.1 trillion in assets across hundreds of portfolio companies. Hellman & Friedman controls a portfolio of mid-market businesses spanning healthcare, technology, and financial services. Goldman Sachs Asset Management has approximately $3.7 trillion in assets under supervision globally. Apollo, General Atlantic, Leonard Green, and GIC together add hundreds more portfolio companies across every major industry.

The joint venture does not need to go find customers. It has a built-in pipeline of hundreds of companies whose existing owners have both the incentive to adopt AI quickly and the authority to compel adoption across their portfolios. By pairing the latest Claude models with a built-in network of investor-owned companies, Anthropic is positioning itself to gain an edge in middle-market adoption of the technology.

This is the piece of the deal that traditional consulting firms cannot replicate. McKinsey can compete on talent and methodology. It cannot walk into a Blackstone portfolio company with the implicit backing of the company's private equity owner. That combination of technical capability and structural access is new.

The OpenAI Mirror

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The Anthropic announcement landed hours after Bloomberg reported that OpenAI is pursuing a near-identical structure.

OpenAI is raising funds for a new venture called The Development Company, backed by TPG, Bain Capital, Advent International, Brookfield, and Goanna Capital. OpenAI plans to raise $4 billion at a $10 billion valuation for the joint venture.

The simultaneous announcement is not a coincidence. Both companies have reached the same strategic conclusion at the same moment: selling model access is not sufficient to capture the full value of the AI transition. The companies that control both the model and the implementation will extract the consulting margin on top of the software margin. Those that only sell the model will be commoditized as models improve and differentiation narrows.

The race to own enterprise AI implementation is now a two-horse race with parallel structures, parallel timelines, and parallel financial backing from the largest pools of private capital in the world. Whichever firm builds the more durable implementation franchise β€” the one that accumulates the deepest institutional knowledge across the most industries β€” will have a structural advantage heading into the IPOs both companies are targeting later this year.

Anthropic's annualized revenue run rate climbed from about $9 billion at year-end 2025 to more than $30 billion by late March 2026, a rise it has attributed in part to AI coding tools such as Claude Code. OpenAI announced $122 billion in new funding at the end of March, against a valuation of $852 billion. Anthropic is in the final stages of its own funding round, seeking $50 billion of new funding against a $900 billion valuation.

It is worth pausing on those numbers. A $900 billion valuation for a company with $30 billion in annualized revenue implies a price-to-revenue multiple of 30x β€” an extraordinary premium that essentially requires Anthropic to sustain its current hypergrowth rate for years without meaningful margin compression. At those multiples, a consulting venture is not just a revenue opportunity. It is a strategic necessity. A company valued at $900 billion that grows its top line at 20% instead of 200% will face a down-round that reprices everything. The "AI consulting wars" are partly about market positioning. They are also partly about generating the sticky, recurring revenue streams that justify valuations that have outrun any reasonable near-term earnings model. Enterprise implementation contracts β€” sticky, multi-year, embedded in the client's core operations β€” are the most durable revenue available in the AI market right now. This is why both companies moved on the same day.

What This Means for Incumbent Consulting Firms

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The announcement is an implicit indictment of traditional consulting, and the industry's leadership knows it.

McKinsey, BCG, Bain, Accenture, and Deloitte have all been building AI practices for the past two years. They have the client relationships, the industry expertise, and the implementation track records. What they do not have is ownership of the underlying model β€” which means they are always one layer removed from the most powerful part of the AI stack, dependent on licensing agreements with the companies now trying to cut them out.

Sequoia partner Julien Bek argued in April that the world's next great company won't sell software at all, but outcomes: legal services, financial analysis, healthcare diagnostics β€” delivered as a managed service rather than a licensed tool. That thesis is exactly what both ventures are betting on.

The consulting incumbents are not standing still. McKinsey has made significant AI infrastructure investments. Accenture has been acquiring AI implementation firms at pace. But their business model β€” billing hours, not outcomes β€” is structurally disadvantaged against a firm that owns the model and can price on results rather than time.

There is one conflict of interest embedded in the venture's structure that nobody has addressed publicly yet, and that lawyers will eventually be paid handsomely to resolve. Blackstone and Goldman Sachs own competing portfolio companies across virtually every industry the venture plans to serve β€” healthcare, financial services, manufacturing, retail. When an Anthropic engineer embeds inside a Blackstone-owned healthcare company and spends six months redesigning its clinical workflow, they accumulate deep institutional knowledge about that company's operations, data architecture, and competitive vulnerabilities. What happens when that same engineer, or the firm's broader engineering team, subsequently deploys inside a Goldman Sachs-owned company that competes in the same market? Who owns the institutional knowledge gained? What contractual walls prevent it from flowing? The venture's press release is silent on this. The lawyers who will eventually litigate it are not yet in the room β€” but they will be.

What This Means for Investors

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The joint venture has several distinct implications depending on where your exposure sits.

For investors tracking Anthropic toward its IPO β€” reportedly as early as October 2026 at a $900 billion valuation β€” the venture is a direct revenue accelerant. Enterprise implementation contracts are higher-margin and more durable than API licenses. A successful rollout across even a fraction of the Blackstone-Goldman-Hellman & Friedman portfolio network would add material recurring revenue to Anthropic's top line before the IPO window opens.

For investors in traditional consulting and professional services β€” Accenture trades at roughly 25x earnings, Gartner at 35x β€” the announcement is a structural threat that the market has not yet fully priced. The consulting sector's AI practices are their highest-growth segments. A well-capitalized, model-native competitor with a built-in client pipeline directly targets those segments.

For investors in the private equity sector broadly, the joint venture signals something about how the largest alternative asset managers are thinking about value creation in their portfolio companies: AI implementation is now a portfolio-level priority, not a company-by-company decision. GPs who move first to standardize AI deployment across their portfolios will have a measurable operational advantage by the time their next fund cycle closes.

The AI consulting wars have begun. The combatants are not the firms you expected.

Sources

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