When large incumbents like Microsoft release features that seem late or inferior to startup versions, it's often not a lack of innovation. They must navigate a complex web of international regulations, accessibility rules, and compliance standards (like SOC 2 and ITAR) that inherently slow down development and deployment compared to nimble startups.
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Large enterprises navigate a critical paradox with new technology like AI. Moving too slowly cedes the market and leads to irrelevance. However, moving too quickly without clear direction or a focus on feasibility results in wasting millions of dollars on failed initiatives.
While not in formal business frameworks, speed of execution is the most critical initial moat for an AI startup. Large incumbents are slowed by process and bureaucracy. Startups like Cursor leverage this by shipping features on daily cycles, a pace incumbents cannot match.
For a founder coding their own product, every minute spent trying a new, unproven tool is a direct opportunity cost against shipping features. This contrasts with developers in larger companies who may have downtime to experiment as a hobby or part of their job.
Incumbents are disincentivized from creating cheaper, superior products that would cannibalize existing high-margin revenue streams. Organizational silos also hinder the creation of blended solutions that cross traditional product lines, creating opportunities for startups to innovate in the gaps.
The historical advantage of being first to market has evaporated. It once took years for large companies to clone a successful startup, but AI development tools now enable clones to be built in weeks. This accelerates commoditization, meaning a company's competitive edge is now measured in months, not years, demanding a much faster pace of innovation.
There appears to be a predictable 5-10 year lag between a startup's innovation gaining traction (e.g., Calendly) and a tech giant commoditizing it as a feature (e.g., Google Calendar's scheduling). This "commoditization window" is the crucial timeframe for a startup to build a brand, network effects, and a durable moat.
Competing in the AI era requires a fundamental cultural shift towards experimentation and scientific rigor. According to Intercom's CEO, older companies can't just decide to build an AI feature; they need a complete operational reset to match the speed and learning cycles of AI-native disruptors.
For incumbent software companies, an existing customer base is a double-edged sword. While it provides a distribution channel for new AI products, it also acts as "cement shoes." The technical debt and feature obligations to thousands of pre-AI customers can consume all engineering resources, preventing them from competing effectively with nimble, AI-native startups.
Simple products like DocuSign become massively complex at scale due to requirements for local data centers, country-specific standards (e.g., Japanese stamps), on-premise appliances for security, and compliance needs like FedRAMP. This complexity justifies a large engineering team.
Founders often over-prioritize non-revenue tasks like getting compliance certifications. Unless you are actively losing deals because you lack SOC 2 or ISO, you should delay it. View compliance as a task to be completed only when it becomes a direct blocker to sales, not as a box to check early on.