To differentiate hype from reality, seed investors should practice "vibe coding": daily, hands-on experimentation with new developer tools. This provides an intuitive understanding of current technological capabilities, leading to better investment decisions and inoculating them against unrealistic expectations.

The trend of 'vibe coding'—casually using prompts to generate code without rigor—is creating low-quality, unmaintainable software. The AI engineering community has reached its limit with this approach and is actively searching for a new development paradigm that marries AI's speed with traditional engineering's craft and reliability.

AI lowers the barrier to entry, flooding the market with "whiteboard founded" companies tackling low-hanging fruit. This creates a highly competitive, consensus-driven environment that is the opposite of a "good quest." The real challenge is finding meaningful problems.

TinySeed identifies "vibe-coding"—using AI to write code without expert engineering oversight—as a major investment risk. This approach leads to unmaintainable code, causing feature velocity to collapse and catastrophic regression bugs within 6-18 months, effectively creating a technical time bomb they are unwilling to fund.

'Vibe coding' describes using AI to generate code for tasks outside one's expertise. While it accelerates development and enables non-specialists, it relies on a 'vibe' that the code is correct, potentially introducing subtle bugs or bad practices that an expert would spot.

As AI generates more code, the developer tool market will shift from code editors to platforms for evaluating AI output. New tools will focus on automated testing, security analysis, and compliance checks to ensure AI-generated code is production-ready.

The core value proposition of no-code platforms—building software without code—is being eroded by AI tools. AI-assisted 'vibe coding' makes it much easier for non-specialists to build internal line-of-business apps, a key use case for no-code, posing an existential threat to major players.

AI drastically accelerates the ability of incumbents and competitors to clone new products, making early traction and features less defensible. For seed investors, this means the traditional "first-mover advantage" is fragile, shifting the investment thesis heavily towards the quality and adaptability of the founding team.

Many companies market AI products based on compelling demos that are not yet viable at scale. This 'marketing overhang' creates a dangerous gap between customer expectations and the product's actual capabilities, risking trust and reputation. True AI products must be proven in production first.