Ways to earn extra income by selling AI tools and services

Ways to earn extra income by selling AI tools and services

Artificial intelligence (AI) is one of the most transformative technologies of our time. With the widespread adoption of AI across industries, the demand for knowledgeable experts in this field is rapidly increasing.

For those with technical skills in AI development or a passion for the emergent possibilities of this technology, there are several ways to earn extra income by leveraging your expertise to sell AI tools or provide consulting services.

Developing and Selling AI Models or Software Tools

One of the most straightforward ways to earn extra income through AI is by developing models, software, or tools and selling them directly to customers. This could involve building specialized machine learning models tailored for certain applications and tasks. It may also mean creating software products that make AI more accessible and useful for non-technical audiences.

Technical Concepts

When discussing the development of AI tools for sale, it’s important to understand some key technical concepts:

  • Machine learning models are algorithms trained on data to perform tasks like classification, regression, or forecasting. Common model types include neural networks, decision trees, and support vector machines. These can be developed and optimized for specific prediction or automation needs.
  • Software engineering principles like abstraction, modularity, and code reuse are critical for developing high-quality AI tools and applications in a maintainable way. Software products should have intuitive user interfaces and documentation.
  • Integration/deployment refers to how developed AI models and software can be smoothly integrated into customer systems and deployed at scale. Considerations include APIs, cloud hosting, and packaging tools for different operating environments.

Practical Considerations

Some practical points to keep in mind when pursuing this opportunity include:

  • Identifying a clear market niche and customer pain points your offering can uniquely address. Conduct user research upfront.
  • Developing a minimum viable product (MVP) quickly to validate demand before extensive development.
  • Pricing models include per-use licenses, monthly subscriptions, or one-time purchases. Consider trial periods.
  • Marketing strategy – focus on relevant blogs, forums, and conferences to build awareness and trial users initially.
  • Customer support obligations like training, updates, and bug fixes that need resources.

Case Studies

Here are a few examples of individuals earning income through AI software/model commercialization:

  • Anthropic raised $30M developing Constitutional AI safety techniques. They also offer paid safety reviews and customized training for enterprise clients.
  • DataRobot marketplace allows independent data scientists to list and sell pre-trained machine learning models earning up to 70% commission per sale.
  • Anthropic PBC, started by Dario and Daniela Amodei, offers AI safety consulting and model auditing services earning $500k+/year, according to their transparency reports.

Developing and directly selling specialized AI capabilities can be financially rewarding if addressing clear needs. With research and community involvement, it’s possible to build sustainable ventures in this space.

Providing AI Consultancy and Professional Services

For those with expertise but less interest in full product development, providing consultancy services is another viable path. Consulting involves advising clients on AI strategy, implementation, and best practices through hands-on problem-solving engagements.

Technical Expertise Areas

Key AI subfields that see demand for consulting include:

  • Machine learning engineering – assisting with model selection, feature engineering, training, and deployment.
  • NLP/chatbots – helping design, train, and integrate conversational systems for applications like customer support.
  • Computer vision – consulting on tasks like object detection, image classification, and automation with visual data.
  • Recommendation systems – leveraging analytics to build personalized recommendation engines for e-commerce sites, content platforms, etc.
  • Data science workflows – advising on data cleaning, ETL pipelines, experiment tracking, and more to enable ML initiatives.

Establishing a Consulting Practice

Some suggestions for setting up a viable consultancy include:

  • Develop case studies highlighting the real impact of prior work to build credibility.
  • Source clients initially through personal networks, conferences, and industry communities.
  • Propose flexible project-based or hourly billing models tailored for budgets.
  • Consider teaming with specialists in complementary domains for larger opportunities.
  • Be responsive, track deliverables diligently, and maintain client relationships for referrals.
  • Continually learn to keep expertise relevant amid AI’s rapid development.

Consulting Success Stories

Many prominent AI researchers successfully transitioned to lucrative consulting careers:

  • Dario Amodei (formerly at OpenAI) founded Anthropic to provide AI safety consulting earning $500k/year from 10 clients.
  • Andrew Trask (formerly DeepMind) founded Anthropic to provide various AI services on a consulting basis.
  • Several professors and researchers list hourly AI consulting rates up to $500/hour on platforms like Kaggle and Quora.

By leveraging specialized skills and experience, AI consulting can be a flexible and financially rewarding path for technical experts. Demand appears strong across industry verticals undergoing digital transformation.

Developing AI/ML Courses for Online Learning Platforms


There is a significant need for high-quality educational resources for skilled workers due to the scarcity of AI talent. Developing courses for online learning platforms has emerged as another avenue for monetizing AI expertise.

Popular platforms pay instructors based on student enrollment and performance.

Common Course Types

Some course categories commonly seen on platforms include:

  • Introduction to AI/machine learning – primers on basic concepts, algorithms, and applications.
  • Specific model/technique courses – deep dives into neural networks, NLP, computer vision techniques, etc.
  • Domain-focused courses – applying AI in areas like healthcare, finance, marketing, manufacturing, etc.
  • Tools/framework courses – teaching platforms like TensorFlow, PyTorch, and sci-kit-learn for practical skills.
  • Career transition courses – assisting working professionals enter the AI field.

Developing an Effective Online Course

Key aspects of creating a successful online course include:

  • Clear and coherent curriculum mapped to learning objectives
  • High-production quality videos, slides, and coding tutorials
  • Balancing conceptual knowledge and hands-on applications
  • Interactive quizzes, assignments, and community forums
  • Maintaining an engaging teaching persona
  • Iterative updates based on student feedback

Income Potential from Online Courses

Popular platforms like Coursera, Udemy, and edX offer 50-70% revenue share on paid course enrollments, sometimes more for specializations. Top instructors can earn:

  • $ 10K-50K/month on Udemy with just a few hundred students
  • $50K-100K+ annually on Coursera, depending on class sizes
  • Supplementary income from publishing course materials/books

By developing sought-after skills-focused content, it’s possible to build an economically sustainable virtual classroom. With community engagement and continual refinement, annual earnings can surpass many traditional jobs.

Providing Managed AI Services

For individuals or startups interested in entrepreneurship over contract work, launching a managed services company can unlock higher long-term profits. These companies develop and maintain AI/ML systems for clients on an ongoing basis.

Common Managed Services Offerings

Typical offerings may involve:

  • Custom AI software development and integration
  • Hosting and maintaining pre-existing models/tools
  • Regular model monitoring and retraining
  • Feature enhancements based on customer needs
  • Management dashboards and reporting
  • Long-term support and maintenance contracts

Starting a Managed Services Company

Challenges include:

  • Seed funding for initial development sprints
  • Sourcing and managing multiple concurrent projects
  • Hiring and leading technical teams long-term
  • Sales and business development

Rewards include higher margins, recurring revenue, and scalable growth.

Case Studies

Thrive AI provides AI consultation and manages ML services for enterprises. Their clients include Fortune 500 brands, earning $1M+ annual revenue.

Anthropic provides sustained AI safety practices for clients like OpenAI to become multi-million dollar startups.

By developing excellent execution skills to deliver ongoing value, managed services can incubate sizable AI businesses over time.

Launching AI-Focused Media/Publishing Ventures

For those interested in educating broader audiences about AI’s potential, launching media/publishing projects represents another monetizable path. Popular forms include:


Insightful writing about the latest research, tools, and applications attracts sponsorships and paid subscribers. Examples: AI Impacts by Future of Life Institute, AI Alignment Forum, and The Gradient by Anthropic.


Conversational deep dives into technical/philosophical AI discussions see sponsorships and Patreon support. Examples: Lex Fridman Podcast, Anthropic Podcast.

Online Courses/Books

Publishing expanded course materials or in-depth books catering to different expertise levels provides income streams through sales. Popular examples: “Hands-On Machine Learning with Scikit-Learn & TensorFlow” by Aurélien Géron.

YouTube Channels

Visual, easy-to-understand explanations of complex concepts attract brand sponsorships and channel memberships, leading to full-time incomes. Examples: Two Minute Papers, Anthropic.

Monetization Strategies

Common models leveraged in combination include brand sponsorships, newsletter/podcast subscriptions, online course/book sales, and affiliates. Engaging large follower bases can catalyze greater earning potential through:

  • Speaking opportunities at events and conferences
  • Consulting/training projects through new connections
  • Higher valuations should the channel wish to pursue other opportunities like acquisitions

By thoughtfully sharing knowledge and sparking interests, AI media/publishing provides not just supplemental income potential but also opportunities to meaningfully shape understanding of this critical technology. Launching does not require large teams – often, a passion for the topic and quality execution are enough to incubate success.

FAQs about Earning Extra Income through AI

Here are answers to some frequently asked questions about different paths discussed:

How do I learn the technical skills required for these opportunities?

The best way to gain proficiency is via online courses from reputable platforms, contributing to open-source projects, participating in AI/ML meetups, pursuing online certifications, reading the latest research papers, and conducting personal projects. Community involvement is highly recommended.

Do I need a technical degree to earn through these methods?

While advanced degrees confer certain advantages, it’s definitely possible to succeed without a CS background so long as strong fundamentals and portfolio projects showcase abilities. Many prominent figures are self-taught.

What is the typical time commitment expected across these paths?

Opportunities like consulting, courses, and media can vary significantly in flexibility, but building client bases/followings takes perseverance – plan for at least 15-20 hours per week minimum. Software/model development and full-time startups mandate larger time investments initially.

What level of earnings can reasonably be expected from each path?

Incomes as high as six figures annually are achievable with success and experience across most discussed paths depending on execution and market factors. Consulting, courses, and software tools offer more consistent revenue potential, whereas startups inherently carry risks. Commonly $10K-$50K could represent attainable supplemental goals.

Is securing first clients/users difficult without reputation/referrals?

Yes, bootstrapping initial momentum presents challenges across all avenues. Leveraging personal networks, communities, targeted content marketing, and trial/freemium models helps lower barriers. Establishing case studies and portfolio work showcasing expertise and value also eases this phase considerably. Patience and persistence are important virtues.

What are some common pitfalls to watch out for across these paths?

Key risks involve overlooking product/market fit, legal/compliance issues, inadequate financial/operations planning, burnout from insufficient boundaries, stakeholder misalignment, and jeopardizing partnerships/workplace culture. Continuous learning, prudent diversification of risks/income sources, and building supportive relationships help mitigate such difficulties.

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