[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Aaron Levie [@levie](/creator/twitter/levie) on x 2.5M followers Created: 2025-07-13 01:13:34 UTC We live in a brief and fascinating moment right now where there’s an insanely wide gap in productivity between two people just based on the tools they use and their specific workflows with AI. Just a few variables can be the difference between getting a XX% boost in productivity with AI or a XXX% boost. Here are just a few that seem to be emerging; * Choosing the right AI model creates a substantial amount of leverage. While models for basic querying in our personal lives have all generally reached the same level of utility, it’s clear there’s still major differences for complex tasks like coding, deep research, medical use cases, and other critical vertical tasks. * Picking the right tool for interacting with an AI model is a major variable because the agentic experiences *and* the AI Agents within these platforms differ so heavily. The system prompts, context that each AI Agent is given, and tool use drive very different levels of performance for any given task. * Understanding the best ways to prompt an AI Agent to maximize the results has a wide amount of variance. This may be the single biggest difference in outcomes in a lot of cases. Some people just type in a few lines and think the AI will take care of the rest, but the pros clearly spend a meaningful chunk of time just getting the prompt to be perfect. * There are also bespoke workflows emerging that are hyper tuned to specific types of tasks. Some people will use a certain model and tool for creating a product spec, then another for writing the code, then another one for reviewing the code, and so on. Another common way of working is to give multiple AI Agents the same task and just compare which delivered the best result. It’s wild that this particular pattern of work is both so effective, and resulting in substantial differences in productivity. Yet here we are. These differences should theoretically converge over time, but for the foreseeable future we’re seeing very different levels of output based on the user’s approach. XXXXXXX engagements  **Related Topics** [coins ai](/topic/coins-ai) [productivity](/topic/productivity) [Post Link](https://x.com/levie/status/1944203516865859950)
[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]
Aaron Levie @levie on x 2.5M followers
Created: 2025-07-13 01:13:34 UTC
We live in a brief and fascinating moment right now where there’s an insanely wide gap in productivity between two people just based on the tools they use and their specific workflows with AI.
Just a few variables can be the difference between getting a XX% boost in productivity with AI or a XXX% boost. Here are just a few that seem to be emerging;
Choosing the right AI model creates a substantial amount of leverage. While models for basic querying in our personal lives have all generally reached the same level of utility, it’s clear there’s still major differences for complex tasks like coding, deep research, medical use cases, and other critical vertical tasks.
Picking the right tool for interacting with an AI model is a major variable because the agentic experiences and the AI Agents within these platforms differ so heavily. The system prompts, context that each AI Agent is given, and tool use drive very different levels of performance for any given task.
Understanding the best ways to prompt an AI Agent to maximize the results has a wide amount of variance. This may be the single biggest difference in outcomes in a lot of cases. Some people just type in a few lines and think the AI will take care of the rest, but the pros clearly spend a meaningful chunk of time just getting the prompt to be perfect.
There are also bespoke workflows emerging that are hyper tuned to specific types of tasks. Some people will use a certain model and tool for creating a product spec, then another for writing the code, then another one for reviewing the code, and so on. Another common way of working is to give multiple AI Agents the same task and just compare which delivered the best result.
It’s wild that this particular pattern of work is both so effective, and resulting in substantial differences in productivity. Yet here we are. These differences should theoretically converge over time, but for the foreseeable future we’re seeing very different levels of output based on the user’s approach.
XXXXXXX engagements
Related Topics coins ai productivity