📊 Full opportunity report: The Forward-Deploy Pivot: Why Anthropic and OpenAI Are Becoming Consulting Firms in the Same Week on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
Anthropic and OpenAI are establishing new enterprise units that embed AI engineers into mid-sized companies, resembling consulting firms. This marks a shift in how AI firms are approaching revenue and market expansion, targeting the mid-market segment and challenging traditional consulting giants.
Anthropic and OpenAI have each announced the creation of new enterprise services units designed to embed AI engineers directly into mid-sized companies, marking a significant shift toward consulting-like operations in the AI industry.
On May 4, Anthropic disclosed a $1.5 billion AI-native enterprise services joint venture backed by major private equity firms and asset managers, aiming to embed its Applied AI engineers into sectors such as healthcare, manufacturing, and finance. The firm’s goal is to replicate Palantir’s forward-deploy model, focusing on mid-market companies that are too small for large consulting firms but too sophisticated for self-service software.
Simultaneously, on May 6, OpenAI announced the formation of ‘DeployCo,’ a similar enterprise-focused entity valued at $10 billion, backed by prominent private equity investors including TPG and Bain Capital, with commitments totaling $4 billion. DeployCo aims to leverage OpenAI’s technology to deliver outcome-based solutions across industries, positioning itself as a new type of consulting firm.
This coordinated timing suggests a strategic effort to capture a larger share of the $6 services-for-every-dollar-software industry, particularly targeting the mid-market segment that is underserved by traditional consulting giants like McKinsey, BCG, and the Big Four system integrators.
Same week.
Two consulting firms.
Anthropic and OpenAI synchronized $5.5B in commitments to rebuild the consulting industry from scratch — backed by ~$10 trillion in aggregate AUM.
May 4 · $1.5B Anthropic vehicle with Blackstone + Hellman & Friedman + Goldman Sachs as founding partners. OpenAI’s “DeployCo” announced hours earlier — $4B at $10B valuation, 6.7× larger. Both use Palantir’s forward-deployed engineering model. Captive customer pipeline through PE portfolio ownership = unprecedented enterprise software moat.
Two ventures. One opportunity.
The most concentrated assembly of private capital ever announced for AI services. Captive customer pipeline through PE portfolio ownership is the structural moat — when the PE firm owns both the services firm AND the customer, traditional buyer-seller dynamics break down.
- Anthropic$300M · founder
- Blackstone$300M · $1.3T AUM
- Hellman & Friedman$300M · $115B AUM
- Goldman Sachs AM$150M · $625B alts
- General Atlantic~$150M · $80B+
- Apollo + Leonard Green+ GIC + Sequoia
overlap
- OpenAI$500M · founder
- TPG$250B+ AUM
- Brookfield$1T+ AUM
- Bain Capital$185B+ AUM
- Advent International$90B+ AUM
- 15 unnamed investors$4B total commits
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Four days. Four layers.
Each layer compounds the others. Compute enables deployment scale. Models provide capability. Templates productize workflows. Services firm provides delivery. PE pipeline provides customers. The blitz is coordinated IPO positioning ahead of Q4 2026.
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Five tiers. Five trajectories.
The disruption is uneven by tier. Indian IT faces structural threat (cost-arbitrage labor model obsolescence). Big Four maintain Fortune 500 dominance. Strategy consultancies durable on judgment work. Palantir’s FDE model gets validation premium.
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Three scenarios. One restructuring.
Whether the captive customer model scales as projected or faces execution constraints. Both vehicles likely achieve material scale rather than one collapsing — the structural setup is overwhelming.
- 1,500-2,500 deploymentsBy end-2027 across portfolio.
- 3-6 month deliveryVs 12-18 months traditional.
- Big 4 mid-market compressesIndian IT down 30-40%.
- JV revenue $1-2B by 2028Material IPO contribution.
- Outcome: October 2026 IPO at $900B+. JV is bull case.
- 800-1,500 deploymentsBy end-2027.
- Bifurcated marketFDE entities + traditional SI both grow.
- Big 4 deepen alt-AI partnershipsAccenture+OpenAI; Deloitte+Google.
- JV revenue $400-800M by 2028Supporting narrative.
- Outcome: IPO proceeds. JV is one of several threads.
- Engineering scaling hardFDE talent the binding constraint.
- PE governance frictionMultiple sponsors create overhead.
- Big 4 defends aggressivelyPricing competition compresses.
- JV revenue $100-300M by 2028Underperforms projections.
- Outcome: IPO valuation hit. Potential 2027 delay.
This is the most aggressive enterprise distribution play in tech history, executed in synchronized fashion within hours of each other, backed by approximately $10 trillion in aggregate AUM. The captive customer move is the new structural moat for AI commercialization. Everything else is supporting infrastructure.
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Four assignments. By role.
Track 90-180 day customer traction.
Anthropic IPO valuation case strengthens materially. The captive distribution channel adds structural multi-year revenue visibility worth plausibly $500M-$2B incremental ARR by Q4 2027. Q4 2026 IPO probability rises from ~50% pre-announcement to ~65-70% post-announcement. Verify execution before drawing valuation conclusions.
Form competing vehicles or cede captive economics.
KKR, Carlyle, Vista, Thoma Bravo, Silver Lake, Warburg Pincus face strategic choice. Form parallel vehicles with smaller AI labs (Mistral, Cohere, xAI) or with Microsoft/Google/Meta as model partners. Or accept structural disadvantage. The captive customer model is the new value-creation default.
Equity-aligned partnerships and vertical specialization.
Big 4 — deepen alt-AI partnerships (Accenture-OpenAI, Deloitte-Google likely). Indian IT — pivot to AI-native delivery aggressively or face 25-40% market cap compression. Mid-market integrators (EPAM, Genpact) face direct competition; vertical specialization in regulated industries (defense, government, large healthcare) is the defensible position.
PE-owned companies face accelerated AI deployment.
If your company is owned by Blackstone, H&F, Apollo, GA, Leonard Green, GIC, Sequoia — direct JV engagement arriving 12-24 months. If OpenAI DeployCo’s PE backers — same. Reskill toward judgment-intensive roles. The Atlassian template applies — workforce composition reshape, not just headcount cut. 15-25% restructuring across PE-portfolio companies over 2026-2030.
Implications for the Future of AI and Consulting
This development signals a fundamental transformation in how AI companies are approaching revenue generation and market expansion. By adopting consulting-like models, Anthropic and OpenAI aim to capture more value from enterprise deployments, especially in the lucrative mid-market segment. This shift could challenge the dominance of traditional consulting firms and reshape the AI services landscape, potentially accelerating AI-driven automation across industries.
Background on AI-native Enterprise Strategies
Until now, AI firms like Anthropic and OpenAI primarily focused on developing and licensing technology, with revenue streams centered on API usage and licensing deals. The recent announcements mark a departure toward embedding engineers directly into client operations, akin to Palantir’s forward-deploy model, which has been successful in government and large enterprise sectors.
Both firms are leveraging their substantial funding rounds—Anthropic is reportedly in final stages of a $40-50 billion round, and OpenAI’s DeployCo valuation is at $10 billion—to fund these new operational models. The strategic timing aligns with broader industry trends toward outcome-based AI solutions and integrated enterprise services.
“The structural shift toward embedding AI engineers into client companies signals a move away from traditional licensing toward a consulting-like revenue model, targeting the underserved mid-market segment.”
— Thorsten Meyer
Unclear Details on Long-Term Impact and Market Adoption
It remains uncertain how quickly traditional consulting firms will respond to this shift and whether AI-native enterprise units will achieve widespread adoption in the mid-market. Additionally, the long-term profitability and scalability of these models are still developing, and regulatory or competitive pressures could influence their success.
Next Steps in AI Enterprise Market Expansion
In the coming months, expect further announcements from both Anthropic and OpenAI regarding client deployments, partnership developments, and potential public listings. Monitoring industry responses, especially from the Big Four consulting firms and traditional SIs, will be crucial to understanding how this structural shift unfolds. Additionally, the success of these ventures could influence broader industry adoption of embedded AI engineering models.
Key Questions
Why are Anthropic and OpenAI creating consulting-like units?
They aim to embed AI engineers directly into client companies to deliver outcome-based solutions, capture more value, and expand their market reach beyond licensing and API revenue.
How does this shift threaten traditional consulting firms?
By offering AI-augmented engineering services directly to mid-sized companies, these firms could capture a significant share of the $6 services-for-every-dollar-software industry, reducing reliance on traditional consultancies.
Will this change how AI companies are valued?
Yes, as these new models focus on embedded enterprise solutions and recurring revenue from operational deployments, they could lead to higher valuations and more sustainable revenue streams.
What industries are targeted by these new enterprise units?
Target sectors include healthcare, manufacturing, financial services, retail, and real estate, focusing on mid-market companies that are currently underserved by existing consulting and software providers.
What are the risks associated with this strategy?
Risks include slower-than-expected adoption, regulatory challenges, and potential pushback from established consulting firms. The models’ profitability and scalability are still uncertain.
Source: ThorstenMeyerAI.com
