Joel Spolsky in 2002 identified a major pattern in technology business & economics: The pattern of "commoditizing your complement", an alternative to vertical integration, where companies seek to secure a choke point or quasi-monopoly in products composed of many necessary & sufficient layers by dominating one layer while fostering so much competition in another layer above or below its layer that no competing monopolist can emerge, prices are driven down to marginal costs elsewhere in the stack, total price drops & increases demand, and the majority of the consumer surplus of the final product can be diverted to the quasi-monopolist.
No matter how valuable the original may be and how much one could charge for it, it can be more valuable to make it free if it increases profits elsewhere.
This pattern explains many otherwise odd or apparently self-sabotaging ventures by large tech companies into apparently irrelevant fields, such as the high rate of releasing open-source contributions by many Internet companies or the intrusion of advertising companies into smartphone manufacturing & web browser development & statistical software & fiber-optic networks & municipal WiFi & radio spectrum auctions & DNS: they are pre-emptive attempts to commodify another company elsewhere in the stack, or defenses against it being done to them.
Here's links to the whole series up to VI: https://lettersremain.com/joel-spolsky-on-strategy/
That's a big "if" for Claude Code, et al.
How does open sourcing a Claude code clone drive adoption of anything that is a monopoly or even commercially related? Instead it seems like an attempt to undermine US AI companies.
That being said I am increasingly skeptical of how the US leaders are converging on creating monopolies and going deeper into the app layer that means they will end up owning everything rather than being the substrate of a competitive and flourishing ecosystem.
If it were up to me I would implement a regulation that 1) AI labs can’t own inference hardware, data centers would be a regulated utility like electricity and the internet required to provide open access third party safety, guardrails and audit, 2) inference providers can’t build apps beyond serving API requests 3) training data sets are required to be open sourced within 3 years of training a model.
What we are doing now is allowing vertical and horizontal integration of the hardware, training, inference and application layer. Last time we did that standard oil ended up owning the rail network, pipelines, oil fields, gas stations and refineries. Go see how that worked out for society.
Even if you consider profit motive, what is the profit motive for corporate contributions to open source? The same applies here.