Source URL: https://www.tomtunguz.com/which-level-ai/
Source: Tomasz Tunguz
Title: What Level of AI?
Feedly Summary: Which level do I want to use AI?
I find myself asking this question more & more frequently & I think the answer means at work I’ll be using many AIs – not just one or two.
AI Level Use Case
Description
Chat-Based AI
Find the best Italian restaurant in the North Beach neighborhood of San Francisco.
In-App AI
Find a document or generate an overview paragraph within Notion.
Browser-Based AI
Deep research queries, such as estimating the market size of data center construction.
Computer-Based AI
Transcribe a video call and upload the notes to an investment memo.
Multiple AI Agents
Newer coding agents (e.g., Codex & Jules) work in parallel on the same codebase.
Why are there so many levels? It depends on the context I want the AI to have.
An individual chat is a Google query. I have a very specific question I want to answer. No other details necessary.
On the other hand, the in-app AIs are context specific. For example, I find myself using Gemini to find questions within my Gmail – ferreting a particular clause from our employee handbook.
When working with deep research queries, I want a browser swarm to scour the internet, finding chestnuts within warrens on the web. “Run a market size calculation on data center construction in the US and estimate how much software spend will be applied there.” A deep research agent might process more than 150 websites.
Sometimes I want to operate across multiple applications in my computer, which is why I want the operating system AI. Take notes from this video call, summarize it, send it to the CRM, and then add my tasks to my task manager.
The last one, the multiple coding agents, they operate in parallel and can work together. So it’s like managing a team and I can accomplish much more than I could managing an individual coding agent.
Like working with a person, an AI succeeds with clear direction & the relevant related information to the task. At some point, one AI may be able to switch between these layers easily, but for now, as a user,
AI Summary and Description: Yes
Summary: The text provides an insightful exploration of various levels of AI usage, detailing how different AI agents serve distinct purposes based on context. This is particularly relevant for professionals in AI and software development, highlighting the increasing reliance on diverse AI applications for efficiency and accuracy.
Detailed Description: The text outlines several levels of AI utilization, emphasizing the different contexts in which AI applications are effective. It illustrates how understanding these levels can enhance productivity and tier functionality in workplaces.
– **AI Levels and Use Cases**:
– **Chat-Based AI**: Used for straightforward queries (e.g., locating an Italian restaurant).
– **In-App AI**: Context-specific applications such as generating documents or summaries in productivity tools like Notion.
– **Browser-Based AI**: Engaged for deep research tasks requiring extensive data extraction and analysis.
– **Computer-Based AI**: Provides functionality such as transcribing calls and integrating notes with other business applications.
– **Multiple AI Agents**: Collaborate on more complex tasks like coding, improving efficiency significantly by handling parallel projects.
– **Context Dependency**: The effectiveness of each AI level varies according to the task complexity and specificity:
– Simple queries fit well with chat-based AI, while more nuanced tasks benefit from in-app AI, which can access contextualized data.
– For large-scale research, browser-based AI can aggregate information from numerous sources efficiently.
– Computer-based AI suits multifaceted workflows, needing integration with multiple applications.
– Multiple coding agents mimic team dynamics, enhancing coding productivity.
Overall, the text encapsulates critical insights into how varied AI functionalities can be leveraged in professional settings, emphasizing the need for clear direction and information to maximize AI effectiveness. This understanding is vital for AI professionals looking to optimize workflows and increase productivity by integrating diverse AI applications into their processes.