Open vs Closed Source for Novel Approaches

Question / Claim

When you've developed a novel approach (faster/cheaper/better than closed-source incumbents), should you open-source or stay closed?

Key Assumptions

• Closed-source incumbents can see and learn from your open-source code, creating asymmetric visibility(high confidence)
• LLM-generated code has weak/no copyright protection for the prompter unless deeply specified(medium confidence)
• The valuable part (the novel insight) is the least protectable under IP law(high confidence)
• Organizational inertia often prevents large incumbents from quickly adopting external innovations(medium confidence)
• Incumbents treat open-source competitors as free R&D—they monitor and integrate or acquire(high confidence)
• Users/buyers see open-source as lower switching risk but worry about long-term maintenance(medium confidence)
• Fast-followers use open-source to de-risk their own market validation before cloning(high confidence)
• Closed source is thermodynamically temporary—information leaks toward openness (entropy)(high confidence)
• AI makes implementation near-free; any code can be regenerated from product description(high confidence)
• In AI-saturated markets, insights leak faster than ever—closed source buys less time than before(high confidence)
• All code eventually commoditizes; the only question is whether you've built a business on top before it does(high confidence)
• Market psychology (dev vs enterprise) matters more than 'objectively correct' open/closed choice(medium confidence)

Evidence & Observations

• Copyright protects expression, not ideas—so novel approaches/algorithms get minimal legal protection(citation)
• Open-source can build ecosystem lock-in and canonical maintainer status as alternative moats(personal)
• Enterprise buyers often prefer closed-source for 'throat to choke'—support contracts and accountability(personal)
• Redis/Elastic/MongoDB playbook: open-source code, close the data—works until cloud providers fork (AWS OpenSearch)(citation)
• Big tech (AWS, Google, Microsoft) can move fast when threat crosses revenue threshold, despite general organizational inertia(personal)
• Physics: Zero marginal cost of replication + non-rivalrous nature of software means scarcity is artificial and temporary(personal)
• Economics: Successful open-source monetizes complements (Red Hat=support, MongoDB=Atlas hosting, GitLab=open-core upsells)(citation)
• AI: LLMs can regenerate ~80% of an implementation from product description in hours, collapsing the protection window(personal)
• Google open-sourced Android to commoditize mobile hardware—strategic commoditization of complements(citation)
• Stripe wins not on technical superiority but on trust/brand—demonstrates non-code moats(citation)

Open Uncertainties

• Does being 'first canonical implementation' provide durable advantage or just temporary lead?
• When is the insight inferrable from product behavior vs. truly hidden internally?
• At what traction level does an open-source project cross the 'threat threshold' for incumbents to respond aggressively?
• How long until 'taste' itself becomes AI-augmentable, eroding that moat too?
• Are there domains where regulatory/compliance moats can be built faster than incumbents adapt?

Current Position