Open vs Closed Source for Novel Approaches

CURRENT POSITION

The open vs. closed question is obsolete. In an AI-saturated world, code is a commodity (regenerable from description), insights leak instantly (visible from product behavior), and implementation is near-free. The only durable moats are: (1) data that improves with use, (2) network effects, (3) trust/brand, (4) speed of taste, (5) regulatory/compliance moats. None of these are protected by closed source—most are accelerated by open source. The razor: Open-source your code. Close your data. Compound your taste. The one exception: if your entire value is a single non-obvious insight exploitable within 18-24 months—stay closed, extract value, exit. But that's arbitrage, not a company.

KEY ASSUMPTIONS

• Closed-source incumbents can see and learn from your open-source code, creating asymmetric visibility
• LLM-generated code has weak/no copyright protection for the prompter unless deeply specified
• The valuable part (the novel insight) is the least protectable under IP law

SUPPORTING EVIDENCE

• Copyright protects expression, not ideas—so novel approaches/algorithms get minimal legal protection
• Open-source can build ecosystem lock-in and canonical maintainer status as alternative moats
• Enterprise buyers often prefer closed-source for 'throat to choke'—support contracts and accountability

OPEN QUESTIONS

• 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?

WANT TO EXPLORE DEEPER?

by Lulzx