---
title: "I Use AI 100 Times Per Hour"
description: "Discover how AI transforms productivity: Using AI 100x per hour vs 144 phone checks per day. Real data shows 3x faster work through AI-powered dictation and editing."
categories: ["fundraising"]
keywords: ["AI productivity","AI dictation","Tomasz Tunguz","venture capital","speech recognition","SaaS tools","AI in the workplace","data analysis with AI","AI coding assistance"]
ai_summary: "Explore how using AI 100 times per hour enhances productivity compared to traditional phone usage."
date: 2024-12-09
lastmod: 2026-07-21
canonical_url: https://www.tomtunguz.com/ai-use-in-one-afternoon/
author: "Tomasz Tunguz"
---

How often do you use AI? I tracked my Sunday workday to find out. Between 4:30-9:00 PM (with a dinner break), I monitored every AI interaction while handling emails, analyzing data, & writing.

If the [average American picks up their mobile phone 144 times per day](https://fortune.com/well/2023/07/19/how-to-cut-back-screen-time/) & we call that addiction, I am using AI about a hundred times per hour. Is AI ten times more valuable than a phone? 

*Speech*
![image](https://res.cloudinary.com/dzawgnnlr/image/upload/bdzodz5dtbkjtuwaihu5.png)

Dictation activity is bursty with breaks for dinner. But looking at 7pm & later, I'm hitting the speech API at least once every 2 minutes but closer to every 90 seconds on average. Around 8:00 PM is when I started to email - massive activity spike.

| Field | Value  | 
| --- | ---: |
| Timestamps | 110 | 
| Total Word count |  1998 | 
| Average Words per Call | 18 | 
| Calls per Hour | 51 | 


On average, the typical transcription contains about 18 words, but more than a quarter of them contain more than 50 words. Those longer tracts are typically entire email responses dictated in one shot. 

[Dictation is 3 times faster](https://tomtunguz.com/running-llms-locally/) than typing, so it saves me an enormous amount of time.  

![image](https://res.cloudinary.com/dzawgnnlr/image/upload/m4qplafttwkogbhyfxn8.png)
Two AIs process my voice : initially a dictation AI and a language AI that edits for brevity & clarity (both of these run on my laptop).

*Coding*
| Field | Value  | 
| --- | ---: |
| Venture Industry Analysis, lines of code | 267 | 
| Speech analysis, lines of code | 121 | 
| AI Chats | 12 | 
| Estimated AI Calls per Hour | 32 | 

Publishing data-driven blog post analysis is a key part from formatting the data to analyzing it using R and then publishing charts. All of this is now predominantly handled with prompts to an AI. 

I can generate several hundred lines of code in 5-10 minutes. With the newer models, I expect this to collapse to 1-2 minutes.

In fact, I find myself growing reliant on the AI to the extent that I no longer remember some of the R syntax, A sign of working at a higher level of abstraction : one of the promises of AI.

Fully focused on work, I'm employing AI roughly 50-100 times per hour. 

Within the last 24 months it's clear that AI has become an essential coworker, perhaps at least as important as a mobile phone, but very likely more critical.

## Choosing the Right AI Model : Cost vs Performance

Beyond tracking how often I use AI, I also track which AI models deliver the best value. I analyzed 150 autonomous task executions from my Asana agent monitor comparing Claude Opus 4.5 versus Claude Sonnet 4.5.

The results show that **choosing the right model matters enormously** : Sonnet 4.5 delivers 86% cost savings while being 36% faster.

### Cost Comparison

{{< email_image src="ezckfcxucpc5htpebwim" alt="Cost per task comparison between Opus 4.5 and Sonnet 4.5" width="540" height="360" >}}

Opus 4.5 costs **$0.064 per task** on average, while Sonnet 4.5 costs **$0.009 per task**. At 50 tasks per day, that's **$1,002 in annual savings**.

### Token Efficiency

{{< email_image src="gaejmnrw0rqxbprzkvsh" alt="Token usage comparison showing input and output tokens" width="540" height="360" >}}

Sonnet 4.5 uses 27% fewer tokens (1,797 vs 2,456 per task), contributing to the cost advantage. The model generates more concise outputs while maintaining quality.

### Performance

{{< email_image src="qeg9ngpig3vcbjtisbdm" alt="Task duration comparison showing execution speed" width="540" height="360" >}}

Sonnet 4.5 completes tasks in 38 seconds on average versus 59 seconds for Opus 4.5 - a **36% speedup**. Faster iteration means more productivity.

### Cumulative Impact

{{< email_image src="mon1tksqfvv3ckncn4bs" alt="Cumulative cost over 150 tasks showing dramatic savings" width="540" height="360" >}}

The cost difference compounds rapidly. After 150 tasks, Opus 4.5 has cost $4.79 while Sonnet 4.5 costs just $0.67. The savings trajectory accelerates with scale.

### Hybrid Strategy

Not all tasks need Opus 4.5. I use a hybrid approach :
- **Sonnet 4.5** (95% of tasks) : Email summaries, task routing, data extraction, routine automation
- **Opus 4.5** (5% of tasks) : Complex analysis, investment memos, architectural decisions, ambiguous problems

This strategy delivers **76% cost savings** versus using Opus exclusively while maintaining quality where it matters most.