Why I Chose GPT-5 and OpenAI for My AI Workflow
When people see how deeply I use AI in my day-to-day, the natural question is: “Why OpenAI? Why GPT-5?”
There are plenty of options out there—open source models, specialized SaaS tools, even custom LLMs. I’ve experimented with a lot of them. But for the way I work—switching between code, strategy, research, and documentation all in the same day—OpenAI is the provider that consistently balances power, flexibility, and trust.
Breadth of Capability
The first reason is simple: GPT-5 can handle the range of what I throw at it.
- One moment, I’m dropping in a
code.txtfile and asking for next steps. - The next, I’m pasting in a client email thread and asking for a draft reply in my voice.
- Later in the day, I’m transcribing audio reflections and asking it to pull out key themes.
I need a model that’s not just good at one domain, but fluent across many. That’s where GPT-5 shines.
Consistency and Reliability
I’ve found OpenAI’s stack to be consistent in a way many alternatives aren’t. I can open ChatGPT in the morning, feed it the state of a project, and know I’ll get back structured, usable outputs that align with how I work.
That reliability matters when you’re juggling clients. It’s the difference between trusting AI to carry some of the load vs. babysitting it to make sure it doesn’t derail you.
Ecosystem Fit
OpenAI’s tools integrate cleanly into the platforms I already use:
- VS Code with Copilot Pro for code.
- APIs for custom projects.
- ChatGPT app for my day-to-day conversations, transcripts, and reflections.
I don’t have to fight with the tools. They fit into my workflow the same way Slack or Google Docs does—always available, always aligned.
Security and Trust
The gist: OpenAI’s privacy stance (not training on my chats, enterprise compliance, encrypted storage) is enough for me to feel comfortable running 95% of my daily work through it. I still practice data hygiene—no raw passwords or regulated info—but for code, strategy, and client docs, it’s secure enough to be a primary tool.
Cost vs. Value
Could I roll my own LLM? Sure. But between the cost of compute, the hassle of fine-tuning, and the time to manage it, I’d be solving the wrong problem. At $20/month for ChatGPT Plus (and more for API usage), the ROI is obvious: I offload hours of mental strain and project overhead every single day.
Familiarity and Momentum
Finally, there’s the fact that I’ve been working with OpenAI since before GPT-4. My workflow has grown up alongside these tools. I’ve built patterns, habits, and frameworks around them. That momentum compounds—I don’t have to stop and rethink the foundation every few months.
Closing Thought
There are always new models coming out. Some are faster, some are cheaper, some are open source. But for me, the decision isn’t just about raw benchmarks. It’s about whether the provider can sit at the center of my workflow—handling code, strategy, writing, reflection, and context switching without me worrying about the seams.
That’s why I chose GPT-5 and OpenAI. It’s not the only option—but it’s the one that lets me move fastest, think clearest, and trust the process.