The Top AI Tools I'm Using To Make Money in 2025

What's Inside This Guide

• My AI Qualifications
• AI Discovery Platforms
• Development & Repository Tools
• Language Models (LLMs)
• Image Generation
• Voice Cloning
• Video Generation
• Music Generation
• The Human Element
• Need Custom AI Strategy?

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.

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 closed-source options, Claude by Anthropic remains my go-to for its reasoning capabilities and fewer hallucinations when building tools.

Manus deserves special mention for extending Claude's capabilities through multi-step processing. For complex applications where a single prompt falls short, Manus can orchestrate sequential operations that deliver more reliable results.

I will note that OpenAI's "Reason" mode (which I think leverages o4-mini) effectively does something similar - breaking a task into subtasks and sicking a team of agents on the problem. Claude's "Extended Thinking" also does this, but so far I've found it sometimes underperforms Manus results.

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 Llama models from Meta AI continue improving with each iteration.

For multimodal interactions, OpenAI's GPT-4o still leads with its combination of image manipulation, generation and processing capabilities. Grok 3 by xAI and Gemini 2.5 Pro (details here) are strong alternatives worth exploring.

For my workflow, the $20 monthly investment in Claude has consistently delivered value that exceeds the cost. 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 (also accessible via Discord) maintains its edge for creative visual generation, particularly for artistic applications. Its inpainting feature provides precise control over modifications that other platforms still struggle to match.

Seed Dream (by ByteDance) has emerged as a serious challenger to OpenAI's image generation, with impressive text handling capabilities. Both are solid options to toy around with.

For open-source alternatives, Flux (also at bfl.ai) and ByteDance's Uno (GitHub repo) show promise but still lag behind their commercial counterparts in consistency and quality.

Voice Cloning: Creating Ultra-Realistic Speech

Voice cloning technology has advanced tremendously in 2025, with both commercial and open-source options delivering impressive results. For professional applications requiring the highest quality and reliability, ElevenLabs remains the industry leader in commercial voice cloning solutions.

Their platform offers unparalleled control over voice characteristics, emotional tone, and natural-sounding dialogue. They offer both text-to-speech and speech-to-speech options. What sets ElevenLabs apart is their attention to subtleties like breathing patterns, micropauses, and vocal inflections that make synthetic voices nearly indistinguishable from human recordings.

For those seeking a powerful open-source alternative, Dia released recently and it kicks ass. This 1.6B parameter text-to-speech model specializes in generating ultra-realistic dialogue in a single pass. What makes Dia particularly valuable is its ability to maintain speaker consistency and produce non-verbal communications like laughter and throat clearing.

Dia can be installed from its GitHub repository and runs efficiently on consumer-grade GPUs. The model can be accessed through Pinokio, making it even more accessible to non-technical users. For project flexibility without subscription costs, Dia provides remarkable quality that continues to improve with community contributions.

Video Generation: The Newest Frontier

Based on both sample videos and my own testing, Kling (also at kling.kuaishou.com) currently delivers the most impressive results for closed-source video generation. Their aggressive update schedule shows a commitment to maintaining that lead.

For open-source options, Frame Pack (project page) just released and shows tremendous potential. It's accessible through Pinokio for easier installation. While it's quality is no match for Kling - it can run on very low performance GPUs. The output is okay for the speed, but it's far from beating WAN - my top Open Source video pick right now that (also available on Pinokio).

Runway deserves consideration for its broader creative toolkit that spans both video generation and editing features. While not quite matching Kling's output quality in my tests, its integrated workflow offers advantages for certain projects.

Music Generation: Getting Better, Not Yet Perfect

In the AI music space, three platforms lead the pack: Udio, Suno, and Riffusion. Each offers different strengths – Udio for vocal quality, Suno for instrumental arrangements, and Riffusion for unique sound generation.

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 sequences typically produces disappointing results.

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.

Need a Custom AI Implementation Strategy?

I've helped dozens of businesses implement these tools in ways that actually deliver ROI, not just technological novelty. Whether you're looking to streamline workflows, enhance creative production, or build custom AI tools, I can help you navigate the options and implement solutions that work for your specific needs.

Unlike generic consultants, I focus on measurable improvements in conversion rates, efficiency, and revenue. My approach combines technical expertise with creative problem-solving to deliver concrete results, not just flashy demonstrations.