Jun's Vision: Augmenting Human Capabilities Through Next-Gen AI Interfaces

Join me, Jun, founding engineer at Tusk, as I share my thoughts on building the next generation of AI interfaces that put humans in the center, augmenting our capabilities, and helping us be more thoughtful and creative.

1. Jun is a founding engineer at Tusk and will discuss building the next generation of AI interfaces.
2. The focus is on AI systems that put humans in the center, augmenting capabilities and helping with thoughtfulness and creativity.
3. It is a collection of ideas Jun has been thinking about, some more speculative, to encourage builders in the space to consider these patterns and principles.
4. In 2025, agents are expected to be everywhere, performing research, browsing for users, and automating tasks.
5. Agent-based tooling and protocols are becoming more sophisticated, with large chunks of knowledge work anticipated to be automated in the future.
6. Many AI agents focus on automating discrete tasks, which is easier to quantify, sell, benchmark, and compare one system against another.
7. Over-reliance on automation can lead to general laziness and atrophy of skills; many high-judgment domains like coding and design still require tight human supervision.
8. The main thesis of the talk is to help humans produce high-quality work instead of attempting to automate complex tasks suboptimally.
9. Introducing ideas for augmentation-based UX, looking at interaction patterns for AI helping users review blind spots, spark creativity, and amplify thoughtful decision-making.
10. Principles for designing AI products to emphasize and grow human capabilities and trustworthy human-AI partnerships will be discussed.
11. Comparing automation and augmentation approaches, with automation writing the entire email or code and sending it on behalf of the user, while augmentation helps users brainstorm key points, sugge
12. In augmentation, the human is still in control, with an AI thinking partner reviewing work and suggesting improvements.
13. The shift in mindset moves responsibility for the task to the AI system, transforming it from an offshore contractor to a team member that grows together with the user.
14. The first core interaction pattern is blind spot detection, which is immediately compelling because everyone has blind spots in their thinking.
15. AI should be designed to reveal something at the right time when a unit of work can be assumed to be ready.
16. Task, an AI testing platform, finds edge cases and bugs based on pull requests; it validates potential issues and surfaces them for review.
17. Users can provide feedback by clicking thumbs up or thumbs down or explaining their reasoning, helping the AI learn from user reviews.
18. The second pattern is cognitive partnership, moving from stateless answering machines to systems that adapt to a user's mental models.
19. Building personalization without being creepy is essential; users need to feel understood but not surveyed.
20. Proactive guidance, the third pattern, is the hardest to get right; it should feel like serendipity and not interruption.
21. To build trust in augmentation systems, trust must be progressive, contextual, and bidirectional.
22. Trust should be built with low-stake suggestions before moving to high-impact decisions, allowing a system to prove itself on small things first.
23. AI should facilitate skill growth, visualize skill development, evolve with the user, and create an emotional connection with users.
24. The future of AI interfaces should focus on becoming more fully human, amplifying intuition, taste, and creativity rather than replacing human capabilities.

Source: AI Engineer via YouTube

❓ What do you think? What are the most critical aspects of building trustworthy AI-human partnerships, and how can we prioritize these elements in designing augmentation systems? Feel free to share your thoughts in the comments!