Conventional wisdom suggests attacking an incumbent's weak points. Serval did the opposite with ServiceNow, targeting its core strength: configurability. By using AI to make customization drastically faster and easier, they offered a superior version of the feature that locks customers in, creating a compelling reason to switch.

Gamma's success ($100M ARR with 52 employees) proves an 'AI-first' approach can challenge giants. By rethinking core products like presentations from the ground up with AI, startups can create delightful, hyper-efficient products and achieve massive scale with a tiny headcount.

General Catalyst's CEO notes a change in enterprise AI GTM strategy. The old model was finding product-market fit, then repeating sales. The new model involves "forward deployed engineering" to build deep trust with an initial enterprise client, then focusing on expanding the services offered to that single client.

Previously, building sophisticated digital experiences required large, expensive development teams. AI and agentic tools level the playing field, allowing smaller businesses to compete on capabilities that were once out of reach. This creates a new 'guy in the garage' threat for established players.

Founders are stuck in a SaaS mindset, selling tools to existing service providers. The bigger opportunity is to build new, AI-first service companies (e.g., accounting, legal) that use AI to deliver a superior end-to-end solution directly to customers.

In the SaaS era, a 2-year head start created a defensible product moat. In the AI era, new entrants can leverage the latest foundation models to instantly create a product on par with, or better than, an incumbent's, erasing any first-mover advantage.

An enterprise CIO confirms that once a company invests time training a generative AI solution, the cost to switch vendors becomes prohibitive. This means early-stage AI startups can build a powerful moat simply by being the first vendor to get implemented and trained.

Traditional software required deep vertical focus because building unique UIs for each use case was complex. AI agents solve this. Since the interface is primarily a prompt box, a company can serve a broad horizontal market from the beginning without the massive overhead of building distinct, vertical-specific product experiences.

AI tools enable solo builders to bypass the slow, traditional "hire-design-refine" loop. This massive speed increase in iteration allows them to compete effectively against larger, well-funded incumbents who are bogged down by process and legacy concerns.