I Started 5 AI Businesses in 2 years, here's what I learned..

Key Concepts

• AI Consulting
• AI Agency/AI Automation Agency (Custom Solutions)
• AI Services (Outcome-Based)
• AI Software (SaaS)
• AI Education
• No-Code Automation Platforms (e.g., Naden, Airtable, Relevance AI)
• Scalable Acquisition Channels
• Domain Expertise
• Product-Market Fit
• Productization of Services

1. Five AI Business Models

The video identifies five realistic AI business models for 2025:

1. AI Consulting: Providing AI guidance to businesses or individuals. Focuses on educating clients on AI and no-code platforms.
2. AI Agency/AI Automation Agency: Designing and building custom automation solutions for businesses. Similar to traditional development or IT agencies, but specializing in AI. The key word is "custom."
3. AI Services: Delivering outcomes, results, and KPIs to clients, using AI to automate and enhance internal processes. Similar to traditional marketing, lead generation, or recruiting agencies.
4. AI Software: Selling a self-serve software tool (SaaS) to businesses, offering features and functionalities.
5. AI Education: Creating courses, communities, or programs to deliver AI skills and guidance.

2. Pros and Cons of Each Model & Best Starting Point

• AI Consulting:
  • Pros: Quick speed to revenue, requires only basic automation skills.
  • Cons: Low revenue cap (around $3-5k monthly), hourly-based pricing.
  • Recommendation: Not ideal as a standalone business; better as a lead generation method for an AI agency.
• AI Agency/AI Automation Agency:
  • Pros: Higher value capture than consulting, requires basic automation skills, clients pay while you learn.
  • Cons: Slower speed to revenue initially (learning sales, market unfamiliarity), revenue cap due to scaling challenges (hiring, management), profit margins similar to traditional agencies (20-30%).
  • Recommendation: Best starting point for most, as it allows learning and building domain expertise.
• AI Services:
  • Pros: Faster speed to revenue (selling outcomes), high revenue cap, higher potential profit margins (AI automation), easier sales.
  • Cons: Requires domain expertise to deliver results.
  • Recommendation: Good starting point if you have existing domain expertise (marketing, SEO, etc.).
• AI Software:
  • Pros: Highest scalability and profitability, potential for thousands of customers.
  • Cons: Slowest speed to revenue, requires technical expertise, domain expertise, and go-to-market experience.
  • Recommendation: Not recommended as a starting point without significant SaaS or startup experience.
• AI Education:
  • Pros: Can be profitable.
  • Cons: Requires technical or domain expertise, scalable acquisition channels, and experience in other AI business models.
  • Recommendation: Not recommended as a starting point; best added on top of an existing AI business.

3. Scaling and Transitioning Between Models

The video outlines strategies for scaling within each business model and transitioning to more scalable models:

• AI Consulting:
  • Scaling: Work with bigger clients, charge higher prices, package consulting, scale a team.
  • Transition: Add implementations to your offer to transition into an AI agency.
• AI Agency/AI Automation Agency:
  • Scaling: Work with bigger clients (mid-to-large businesses), add consulting and training to value proposition, improve hiring, management, and SOPs.
  • Transition:
    • AI Education: Add an AI education product based on agency experience.
    • AI Services: Build domain expertise by targeting specific industries (e.g., agencies) and building outcome-based solutions.
    • AI Software: Niche down on one industry, focus on productizable projects, resell solutions, and eventually build a self-serve product.
• AI Services:
  • Scaling: Provide better outcomes, automate more processes, improve hiring, management, and SOPs.
  • Transition:
    • AI Education: Add an AI education business on top of the service business.
    • AI Software: Automate as much as possible of the service delivery and productize it into a self-serve product.
• AI Software:
  • Scaling: Build a better product, achieve product-market fit, increase pricing, scale acquisition.
  • Transition: Launch an AI education business, building communities and education around the product.

4. Examples and Case Studies

• Morningside (Liam Modley): AI agency transitioning to heavily invest in consulting and training.
• Cold IQ: Lead generation agency using AI to enhance outbound email campaigns, achieving better results and higher margins. Grew to over $6 million in annual revenue in two years.
• Superside: AI content agency that grew to $60 million in annual revenue with a relatively small team.
• 11X: AI BDR software valued at $350 million after 2 years.
• 9X: B2B AI education on no-code tools, transitioned from an automation agency.
• Hopp Copy: Newsletter agency that productized its service into a self-serve product.
• Gloria AI: Meta ad agency that productized its service into a self-serve product.
• Aura AI (Yonas): Built voice agents for hotels, starting with custom solutions and transitioning to a self-serve AI SaaS.
• Perspective: Funnel builder that invests heavily in its community to help users build better funnels.

5. Notable Quotes

• "Clients will come to you with things they want to automate. You can learn while working together with clients and get paid while learning basically." (Regarding AI Agency)
• "The key thing in my experience has been to first of all work with bigger clients...Instead, you should work with a similar amount of clients but bigger and bigger clients that pay you higher and higher project prices or higher retainers." (Regarding scaling an AI Agency)
• "More and more of traditional services can now be productized with AI, meaning it becomes far more accessible to SMBs and smaller businesses." (Regarding the transition from AI Services to AI Software)

6. Technical Terms and Concepts

• No-Code Automation Platforms: Tools like Naden, Airtable, and Relevance AI that allow users to build automations without coding.
• SaaS (Software as a Service): A software distribution model where a third-party provider hosts applications and makes them available to customers over the Internet.
• BDR (Business Development Representative): A sales role focused on generating new leads and opportunities.
• Product-Market Fit: The degree to which a product satisfies market demand.
• SOPs (Standard Operating Procedures): Documented procedures for performing specific tasks.
• Lead Magnet: A free offering used to attract potential customers.

7. Logical Connections

The video establishes a clear progression between the different AI business models. It argues that starting with an AI agency is often the best approach because it allows individuals to gain experience, build domain expertise, and identify opportunities for transitioning to more scalable models like AI services or AI software. The AI education model is presented as a natural extension of the other models, allowing businesses to leverage their expertise and build communities around their products or services.

8. Data and Statistics

• AI consulting is difficult to scale past $3-5k monthly revenue.
• Traditional service agencies and development agencies generally have profit margins around 20-30%.
• Cold IQ grew their AI lead generation agency to over $6 million in annual revenue in two years.
• Superside grew to $60 million in annual revenue with a relatively small team for an agency type business.
• 11X is valued at $350 million after 2 years.

9. Conclusion

The video provides a practical guide to navigating the AI business landscape. It emphasizes the importance of starting with a solid foundation (often an AI agency), building domain expertise, and strategically transitioning to more scalable models as opportunities arise. The key takeaway is that success in the AI business world requires a combination of technical skills, business acumen, and a willingness to adapt and evolve.