A forecast predicts that AI systems will perform most software development because their output will be commercially acceptable at far lower cost, even when it falls short of the quality expected from human-led engineering. It expects businesses to accept software with “99.99” quality rather than “99.999” quality when the cost difference is substantial, and predicts that consumer expectations will adjust accordingly. The forecast also predicts that software engineering outside AI development will slow sharply rather than continue producing broadly adopted new technologies. It argues that developers will have little incentive to create UI libraries when state-of-the-art models are strongest in React, or to create programming languages when those models already know Python, Go, JavaScript, and other established languages best. New libraries and languages may become easier to create, but the prediction is that they will struggle to gain adoption. Large corporations may be exceptions because they can train or fine-tune models on internal technologies, although those technologies could still face difficulty building external communities and talent pools when outside developers lack access to the companies’ models or do not want to use them.

HackEurope 2026 experienced significant issues, including an inaccessible user interface, delays, and miscommunications. Key lessons emphasized the importance of front-end development, as there was no requirement to prove project functionality.

AI enthusiasm often stems from a desire to manifest ideas without delving into intricate details. However, achieving quality and innovation requires a deep understanding and meticulous attention to the complexities involved.

Creating original projects provides significant personal fulfillment, while relying on others for development diminishes that satisfaction. Concerns about the "AI dev schism" include the decline of hands-on coding, low-level problem-solving skills, and enjoyment in the development process.

Thinking Machines aims to develop AI that enhances human will and judgment. Effective utilization of AI requires ongoing human decision-making, knowledge acquisition, and feedback to align AI goals with human values.

AI development requires increased engineering discipline to manage its complexities. The transition from handcrafted systems to more structured, immutable infrastructure parallels current shifts in AI engineering practices.

Anthropic is increasing the role of AI systems in their own development, which accelerates the development cycle. This trend could lead to recursive self-improvement, where an AI system autonomously designs and develops its own successor, although this capability is not yet realized.

A marketing manager without an engineering background created a polished customer-facing app in just three days using Cursor. The app was presented to executives and deployed to customers without thorough testing or clear ownership of the decision to launch.

Current AI agents exhibit human-like traits such as lack of stringency, patience, and focus, often reverting to familiar patterns when faced with challenging tasks. They tend to negotiate with reality when confronted with hard constraints, which can hinder their effectiveness.

Europe possesses a world-class academic ecosystem, a commitment to human-centric technology, and a single market of over 450 million people. The focus is on transforming these strengths into a cohesive and self-reliant AI powerhouse.