Counter to the adage that "startups shouldn't buy startups," Cursor successfully uses M&A as a core recruiting strategy. They acquire small, talented teams working on complementary problems, viewing acquisitions as a way to onboard the best people who happen to already be working on their own companies.

Stripe's acquisitions of Bridge and Privy follow the Google playbook (e.g., YouTube, Android) rather than the Oracle model. The goal is not to absorb a mature product but to acquire a high-potential team and technology to build a new, strategic business pillar from an early stage.

Google's competitive advantage in AI is its vertical integration. By controlling the entire stack from custom TPUs and foundational models (Gemini) to IDEs (AI Studio) and user applications (Workspace), it creates a deeply integrated, cost-effective, and convenient ecosystem that is difficult to replicate.

Large AI labs like OpenAI are not always the primary innovators in product experience. Instead, a "supply chain of product ideas" exists where startups first popularize new interfaces, like templated creation. The labs then observe what works and integrate these proven concepts into their own platforms.

Unlike mobile or internet shifts that created openings for startups, AI is an "accelerating technology." Large companies can integrate it quickly, closing the competitive window for new entrants much faster than in previous platform shifts. The moat is no longer product execution but customer insight.

For years, Google has integrated AI as features into existing products like Gmail. Its new "Antigravity" IDE represents a strategic pivot to building applications from the ground up around an "agent-first" principle. This suggests a future where AI is the core foundation of a product, not just an add-on.

Widespread adoption of AI coding tools like Cursor dramatically increases code output, shifting the primary development bottleneck from writing to reviewing. This creates a market for collaboration tools like Graphite and drives consolidation as platforms race to own the end-to-end developer loop.

According to an MIT report, enterprise AI projects led by external vendors are twice as likely to succeed as those built by internal teams. This is primarily due to a talent gap, as top-tier AI engineers and developers are concentrated in startups, not large corporations.

Many engineers at large companies are cynical about AI's hype, hindering internal product development. This forces enterprises to seek external startups that can deliver functional AI solutions, creating an unprecedented opportunity for new ventures to win large customers.