Moving beyond AI Hype - 2 Promising AI Use Cases with Super Small Teams

Assume AI business is only for billionaires? Discover proven models with 1-5 person teams delivering profits and streamlined services wrapping around ChatGPT. No code required. Episode #30
Transcript

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00:00:00 Introduction   

- Opportunity for small teams to build promising AI businesses   

- Market will explode in hype in 2 years  

00:01:05 Video Creation Use Case   

- Estimated costs & risks

00:04:00 The Business Costs of Creating smaller AI Apps

00:06:07 INVIDEO AI video AI demo

00:08:01 Podcast Transcription Use Case   

- PodNotes Founder wrapped GPT for niche need    

- Lower costs than video AI 

00:11:45 Big Tech Threat to Smaller AI Apps

00:14:25 Key Viewpoints   

- Build niche monthly fee businesses   

- 20%+ profit margins

- Action Plan (see end of this page)

AI is taking over, yet hype still overwhelms practical reality. In this episode, Cut through the hype to showcase two promising AI use cases powered by small teams of just 1-5 people.

Discover how new video creation tools and automated podcast transcription services solve problems for customers while building sustainable businesses.

Now is the optimal time for entrepreneurs and startups to harness AI for focused needs before the market explodes with overblown expectations in the next two years.

By charging monthly subscription fees to niche audiences, agile founders can architect over 20% profit margins even on a small scale.

However, risks still abound around assimilation of AI “Agents” from large tech firms and the sustained value of narrow AI applications.

Early movers who obsess over delighting users with an “AI people first” mindset will find the most success.

  • The emergence of AI agents like PodNotes for specific tasks in video and podcasting.

  • The practical use of GPTs and chatbots for business purposes, emphasizing the need to focus on doing one thing well.

  • Considerations around the costs and business models associated with using AI tools for business purposes.

The discussion offers a nuanced view of the opportunities and challenges associated with integrating AI tools into business operations.

Sections:

1. Introduction to Promising AI Use Cases

2. Video Creation Use Case

3. Podcast Transcription Use Case

4. Key Viewpoints

5. Outline

6. In-Depth Discussion Summary 

7. Action Plan

Introduction to Promising AI Use Cases

The podcast discusses two promising AI use cases that small teams of 1-5 people can leverage to build businesses:

1. Video creation using INVIDEO AI’s video AI

2. Podcast transcription/summaries using PodNotes

Today is a good time to build AI businesses before the market explodes with investor hype in 2 years. Start by creating niche products that solve specific problems, charge monthly fees, and aim for at least 20-25% profit margins.

Video Creation Use Case

INVIDEO AI's video AI allows users to generate custom AI videos by providing text prompts. We demonstrate by creating a video on "why people could be the center of AI development."

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Behind the scenes, INVIDEO AI has licensed APIs like GPT to power the video creation.

There are still some limitations around relying on clip art libraries for visuals. Larger players like OpenAI or Adobe could eventually offer similar competing products.

Here’s a breakdown of potential costs to build a similar video AI business:

- Licensing GPT API: $$

- Development team: $2k to $25k 

- Designer: $500 to $5k

- Ongoing server fees

Podcast Transcription Use Case

PodNotes offers automated podcast transcriptions, summaries, audiograms and more. A single founder built the company by wrapping GPT APIs around text-focused transcription tasks.

The service charges just $20 to $50/month for seemingly high value.

The founder likely operates profitably because compute costs are lower for text vs. video AI. There is also a 30% affiliate program, showing healthy margins.

Key Insights

- Now is the time to build promising AI businesses before hype cycle

- Focus on solving niche problems and charge monthly fees

- Aim for over 20% profit margins to sustain the business

- Be cautious of assimilation risk from big tech copying innovations

Outline

1. Introduction

   - Opportunity for small teams to build promising AI businesses

   - Market will explode in hype in 2 years  

2. Video Creation Use Case

   - INVIDEO AI video AI demo

   - Estimated costs & risks

3. Podcast Transcription Use Case

   - PodNotesFounder wrapped GPT for niche need 

   - Lower costs than video AI 

4. Key Viewpoints

   - Build niche monthly fee businesses

   - 20%+ profit margins

5. Action Plan

How to apply what you’ve listened to…

We explore in depth the costs, risks, and opportunities in both the video creation and podcast transcription use cases.

For video AI, big players like OpenAI, Google and Adobe could eventually offer competing products that absorb the innovation. The current solutions also rely heavily on clip art libraries for visuals until better generative video models emerge.

However, video creation remains a promising use case because demand for video content continues to surge. The addressable market is large.

For podcast transcription, risks seem lower because it is focused on text generation rather than video.

Compute costs scale better for text which enables healthier profit margins. There is also less risk in the short term as big tech companies have not targeted this specific customer niche with GPT applications.

In both cases, take an "AI people first" approach when bringing AI/ML products to market. The human element remains important rather than solely leading with technology.

Action Plan 

Based on the promising AI use cases presented, here is one approach to try:

1. Validate Needs: Conduct customer discovery interviews to identify an underserved niche with problems AI could help solve.

2. Size the Opportunity: Research the target market size and growth potential. Video creation and podcasting transcription seem scalable now. 

3. Develop Prototype/MVP: Start small by wrapping GPT for the focused use case. Spend time on the prompt engineering and user experience.

4. Price offering: Price monthly subscriptions ~$20-50 range based on comparable services, factoring in compute costs.

5. Market: Leverage affiliate programs and marketplace lead generation while building an audience and brand.

6. Scale Carefully: Reinvest profits into hiring small teams to incrementally improve the product.

7. Delight Customers: Take an "AI people first" approach when modifying or announcing new capabilities.

Video creation tools and automated transcription reflect only a glimpse of what’s achievable by small teams leveraging frameworks like GPT-4.

As barriers to experimenting with AI continue falling, more focused applications will emerge.

The resilient founders will listen to users first rather than fixating on technology.

They continually reassess assumptions as compute costs, platform risks, and market competition evolves.

 Companies obsessing over delighting customers with hybrid human+AI capabilities stand the best chance of capitalizing on wave after wave of progress in coming years.

Large enterprises may grab headlines, but regular small businesses pioneering promising applications have upside.

Resources

Invideo AI: invideo.io

Podnotes: podnotes.app

Why We Need a People-first AI Strategy

https://knowledge.wharton.upenn.edu/article/people-first-artificial-intelligence-strategy/

Ben's Bites (AI Newsletter, excellent)

https://bensbites.beehiiv.com/

How much would it cost to develop app with ChatGPT?

https://www.altamira.ai/blog/chatgpt-application-development-price/

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The AI Optimist
The AI Optimist
Moving beyond AI hype, The AI Optimist explores how we can use AI to our advantage, how not to be left behind, and what's essential for business and education going forward. Each week for one year I’m exploring the possibilities of AI, against the drawbacks. Diving into regulations and the top 10 questions posed by AI Pessimists, I’m not here to prove I’m right. The purpose here is to engage in discussions with both sides, hear out what we fear and what we hope for, and help design AI models that benefit us all. .