We study double auctions for markets in which goods are valuable in bundles, such as data, model weights, and fine-tuned AI assets. A key friction in such markets is No Assembly: a platform may be unable, for legal or technical reasons, to combine components supplied by different sellers into a single bundle. We formulate a combinatorial buyer's-bid double auction under this constraint. Under explicit stability and price-influence conditions (maintained in general, and for two goods derived from local price-taking and a feedback bound), each bundle submarket inherits the large-market discipline of single-good double auctions: bid shading vanishes, and clearing prices concentrate on competitive levels and track the common value (price discovery). The key incentive step, that bidding on a bundle creates no first-order strategic distortion beyond the single-good logic, is proved for two goods; for larger item sets it remains a maintained condition. Multi-agent reinforcement-learning simulations decompose the welfare loss and indicate that No Assembly, not strategic shading, is the binding finite-market friction, with both losses small in moderately thick markets and declining with complementarity amongst goods.
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