The Top AI Tools You Can Use To Make Money in 2026

My Qualifications: Making a Living With AI

I've made a living using AI tools since 2023. This comes from leveraging emerging tools and sometimes creating my own systems. I'm a musician, so a lot of my experiments have leaned into that experience. Like Music Made Pro - which helps people change song lyrics with the original voice.

Am I a machine learning engineer? ABSOLUTELY not. But I've generated revenue with these tools, optimized workflows, and learned what actually works versus what's just marketing hype. And holy hell - there a lot of hype.

To be upfront - MAKING MONEY STILL TAKES WORK.

Why? Because the moment something is easy to do - everyone and their brother does it. Then the value drops to $0. As a music man - I'm all too familiar with this fundamental lesson of economics.

This list isn't comprehensive – it's curated from tools I've personally tested and found valuable for real business applications. There are a ton of animation, deepfake, and content creation tools I've ommitted for the sake of brevity. The landscape changes weekly, and that constant evolution is precisely why staying informed matters more than chasing every new launch. 🧠

AI Discovery Platforms: Staying Ahead of the Curve

One of the best AI discovery platforms isn't a tool but a YouTube channel: AI Search. They cover experimental projects, content creation tools, and emerging applications across nearly every category.

The real advantage here isn't just learning about new tools – it's understanding the rapid cycle from paid closed-source solutions to free open-source alternatives. That three-month window between corporate release and community adaptation can save you thousands if you time your investments right.

Development & Repository Tools: Simplifying Technical Complexity

Pinokio has transformed how I interact with GitHub repositories. It functions like a specialized browser that handles the installation headaches for you.

Even though I'm capable of manually installing repositories, Pinokio ELIMINATES those tedious setup steps that eat into productive time. It's particularly valuable for accessing open-source models without wrestling with dependencies and environment configurations.

As developers increasingly port their libraries to Pinokio, it's becoming the fastest path to using cutting-edge open-source AI without the technical overhead. For anyone intimidated by command-line interfaces and package management, this tool removes major barriers to entry.

Workflow Automation: Connecting Your Tools

Make.com has become my go-to for workflow automation - it lets you connect different services and automate repetitive tasks without writing code. n8n is a solid open-source alternative if you prefer self-hosting or need more flexibility.

SEO Tools: Search Engine Optimization

Ubersuggest has some solid AI features and I like it for doing search engine optimization stuff - though I'll be honest, I don't spend as much time on SEO as I probably should. But when I do dig in, this tool helps surface keyword opportunities and content gaps.

Meeting Tools: AI-Powered Productivity

Google Meet's automatic transcription and summary feature is pretty great. Built right into Google Workspace, it captures everything discussed and generates actionable summaries without needing a third-party tool.

Serverless Compute: Running AI in the Cloud

Modal deserves a mention for anyone who needs to run compute-intensive AI workloads without managing infrastructure. It's particularly useful for batch processing, model inference, and anything that needs to scale up temporarily then scale back down.

I wrote about my experience building GPU-powered AI video tools with Modal - it's a solid case study if you want to see how this works in practice.

Audio Stem Separation: Isolating Vocals and Instruments

Ultimate Vocal Remover (UVR) is my pick for stem separation. LALAL.AI is good too, but I prefer not paying, and while the Reformer models in UVR aren't quite as polished as LALAL's, they're close enough for most uses.

Language Models (LLMs): The Core of Modern AI

No AI discussion is complete without covering LLMs, though they represent just a fraction of what's possible. Mainly used for coding. And written content - it helps me write blogs like the one you're reading OR video scripts.

For research and sourcing topics, Gemini by Google has become my go-to. Its deep research capabilities and ability to pull from current sources makes it invaluable for understanding complex topics quickly.

For coding, Claude Code by Anthropic has been my primary tool - though I'm considering shifting focus to Gemini 3 since it seems super impressive at coding logic. The landscape is shifting fast in the coding assistant space. If you want to see Claude Code in action, check out my case study on using it for AI data analysis.

On the open-source front, your GPU will largely determine your options. DeepSeek (chat version here) underperforms compared to Claude for coding tasks in my testing, but offers solid capability for many applications.

The right model depends entirely on your specific needs – coding, content creation, data analysis – rather than any universal "best" option.

Image Generation: Visual Creation Gets Even Better

Midjourney is hard to beat for creative visual generation, particularly for artistic applications. Its inpainting feature provides precise control over modifications that other platforms still struggle to match.

For open-source alternatives, Flux from Black Forest Labs is developing some of the best open-source image generation and editing options available. They're consistently pushing the boundaries of what's possible without proprietary systems.

Voice Cloning: Creating Ultra-Realistic Speech

ElevenLabs is still probably the best dialog generator for professional applications. Their platform offers unparalleled control over voice characteristics, emotional tone, and natural-sounding dialogue with both text-to-speech and speech-to-speech options.

That said, many of the open-source options are just as good now, so I'd probably recommend them for most use cases. Dia (by Nari Labs) is excellent for generating ultra-realistic dialogue - it maintains speaker consistency and produces non-verbal communications like laughter and throat clearing. Zonos (by Zyphra, also on HuggingFace) is another strong open-source option worth exploring.

Both can be accessed through Pinokio, making them accessible to non-technical users without wrestling with dependencies.

Video Generation: Honorable Mentions

I haven't used AI video a lot - mainly because it has more of the "slop" effect where people recognize it as AI easily. This can be fine for memes and comedic production, but should likely be avoided in commercial settings unless you dial it in with tools like ComfyUI and multiple specialized models.

For open-source video generation, HunyuanVideo (by Tencent) and Wan 2.1 (by Alibaba) would likely be my picks.

For closed-source options, Sora by OpenAI or Veo 3 by Google would probably be the top contenders right now.

Music Generation: Getting Better, Not Yet Perfect

In the AI music space, Suno leads for really good music generation, while Udio excels at inpainting specific lyrics to change them (though that won't be relevant to most people - but if it is, check out my case study on automating lyric swaps with Udio). Riffusion has rebranded to Producer.ai - I generally don't use it much anymore because they force you to use a chat interface, and the LLM frequently misunderstands what I'm asking for.

These tools excel at creating song covers, generating new compositions, and modifying lyrics while maintaining the original artist's voice characteristics. I've also used them to generate replacement stems for music production work.

The crucial limitation: AI music hasn't yet reached 48 kHz studio quality. It works wonderfully for background tracks and demos, but for professional soundtracks, you'll still want human musicians or significant post-processing. The technology improves monthly, but we're not quite at the replacement stage for premium audio production.

The Human Element: Critical for Quality Results

The most important insight I've gained working with these tools: effective AI implementation isn't about replacement – it's about enhancement. Simply chaining tools together in automated n8n (or Make.com) sequences typically produces disappointing results.

Key insight: The most successful AI users are the ones who combine multiple tools to create better products that don't rely on pure AI outputs. Raw AI generations are becoming easier to spot - the magic happens when you use AI as one component in a larger creative or business process.

Quality assurance at each step remains essential. The real power comes from having humans with domain expertise review and refine what AI produces. A graphic designer will spot image flaws that a programmer might miss, just as a writer will catch narrative inconsistencies that escape a marketer's notice.

Despite marketing claims about eliminating team members, I've found building systems around skilled humans who can scrutinize and improve AI outputs yields FAR superior results. The best implementations use AI to handle repetitive tasks while directing human attention to areas requiring judgment and creativity.