Can AI models create highly technical, personalized solutions for institutional clients, such as integrating into a firm’s tech stack?
I’m not an expert in crypto, but models will fail a lot—about 70% of the time. The goal isn’t to prevent failures but to make them happen quickly so we can learn. Models are highly parallelizable; you can spin up dozens, test many approaches, discard what doesn’t work, and feed the results into the next model. By iterating fast, you eventually get a solution that works for complex, technical use cases.
It's easy to get started with AI communication because the companies building these models intentionally make them accessible so anyone can get decent results without being a prompt engineering expert. However, the real advantage comes from skill: due to a power‑law relationship, improving your prompting ability from the 90th to the 99th percentile can roughly double the quality of the AI’s output. In other words, small gains in skill produce large gains in results. While the basics are simple, mastering AI prompting is hard, and because AI is a meta‑tool that enhances all other tools, becoming proficient at it makes you effective across many domains.
I understand the concern about big model companies dominating, but there are two main types of improvements happening right now: hardware and software algorithmic improvements. Hardware advances mean larger server clusters and compute resources, which let companies train bigger models faster. Software improvements—quantization, inference hacks, and other algorithmic tricks—make models run faster and smaller. Because of these algorithmic gains, especially from teams that lack massive capital, we can run efficient models locally. For example, the Chinese models like GLM 5.2 are highly optimized and can be run on a consumer laptop: a 1‑bit quantized version runs on a MacBook with 256 GB of RAM. In the next six months we’ll likely be able to run comparable models on phones. So the fear of being locked out is overstated. The simplest way to get started is to download Ollama, a platform that lets you run open‑source models locally. You can pull models such as Ornith, GLM 5.2, or Kimmy K 2.7‑code, and many community members provide quantized versions that run on modest hardware. Even if a model is too large, you can pay a few dollars for a hosted service to help set it up, and you’ll be fine.
Start by building rapport and making the prospect laugh, then ask why they agreed to the call and what problems they're facing. Discuss how your solution would work from an outside-in perspective, propose a reasonable solution, and provide social proof by referencing similar systems you've built for others. Follow with a demo flow to show the solution in action, then send a proposal outlining next steps, cost, and scope. This approach productizes your solution using a template as an MVP, and you can reuse it for any automation service. Encourage joining Maker School for assets and support.
AI‑generated automation templates will undercut builders who lack business skills, turning them into mere API endpoints and increasing competition while lowering the barrier to entry. My agency model remains profitable, and for someone just starting, it’s definitely worth the effort to learn and use these tools.