OpenAI & Anthropic's new forward deployed grift
Rent seeking in mid market AI
Last week at basically the same time Anthropic & OpenAI announced joint ventures with major PE firms.
Anthropic: ~$1.5B. Blackstone ($300M), Hellman & Friedman ($300M), Goldman Sachs ($150M), Anthropic ($300M), plus Apollo, General Atlantic, Leonard Green, GIC, & Sequoia.
OpenAI: $10B valuation. $4B from 19 investors anchored by TPG Brookfield, Bain Capital, Advent, Dragoneer, SoftBank, Palantir, Goldman Sachs, Snowflake.
Both adopt the trendy Palantir forward deployed engineer model. Small technical teams embedded inside portfolio companies to rewire workflows. The combined investor base controls thousands of mid-market companies.
Why this is happening
The firms involved hold direct equity in the model companies themselves.
Blackstone holds ~$1B in direct Anthropic equity across multiple rounds. TPG sits in Anthropic’s cap table and anchors OpenAI’s DeployCo. Goldman Sachs is in Anthropic’s Series G, on OpenAI’s revolving credit facility, & committed $150M to the Anthropic join venture. Dragoneer coled Anthropic’s Series G & invested in DeployCo.
The firms benefit at all levels of the transaction but not where they are supposed to:
Direct equity in the model company: appreciation tied to revenue growth & IPO valuation
Equity in the deployment vehicle: implementation fees and guaranteed returns
Control of the portfolio companies being told to adopt the product
The PE firm mandates adoption at the portco. The portco pays the model company and their own owner. Implementation revenue accrues to the model company. Model company revenue inflates the PE firm’s direct equity ahead of IPOs both companies are targeting in 2026. Every dollar of portco spend is serving multiple positions on the same balance sheet.
The position generates returns for the owners but none depend on whether the implementation creates operational value at the portfolio company. It seems pretty early in a new tech hype cycle to already see this level of rent seeking as returns are extracted from positions without a focus on you know… successfully implementing AI.
The old SV playbook
Silicon Valley ran a slimmed down version of this for years. Big VCs encouraged portfolio companies to buy each other’s software inflating ARR ahead of fundraises & IPOs. The practice drew criticism, but the exposure was a lot more limited. The 2026 version is at a much more damaging scale.
The old VC cross-sell inflated metrics inside a smaller system of mostly consenting participants and had the added value of helping each other as design partners. Branching outside of software companies selling to software companies, there are bigger implications to the mid market economy.
17.5% Guarantee
OpenAI contractual returns made the nature of this deal even more explicit if it wasn’t already. DeployCo guarantees PE investors 17.5% annual return for five years. If DeployCo underperforms, OpenAI covers the gap. 17.5% on $4B = up to $700M/year in payout.
It’s pretty clearly purchased customer acquisition structured as a financial instrument. The return is contractual and not performance based so it doesn’t matter whether a single implementation actually creates value for the underlying portfolio.
Who pays the bill
Mid market Portcos. The forward deployed engineers arrive to automate without a clear alignment on goals and outcomes. The owner of these initiatives sits one level down from the pushers. When a project doesn’t have good mutual incentives the outcomes are almost always worse.
Application layer startups. Every vertical AI company selling into mid market PE backed companies is now fighting an uphill battle to sell into this space. You can’t win a competitive evaluation that never happens. The deployment companies and associated circular deals are a moat against the entire application layer of new companies.
Public market investors at IPO. The biggest exposure. Both IPOs will be priced on enterprise revenue manufactured through captive distribution. When the PE hold periods end & the mandates fade, what happens to retention? Index funds, retail investors, & pension funds hold that paper.
What this means for AI
The best AI implementations I've seen start with a specific operational problem and work backward to the implementation. These start with a capital structure and work backward to justify it. The mid market companies powering the economy and writing the checks for AI agent deployments deserve better than being a line item in the AI capital arms race.