The Case for AI Monogamy (Sort Of)
The memory, integrations, and daily workflow that matter when you're ready to stop pasting the same instructions into three different chatbots
I keep three AI chat tabs open every day: ChatGPT, Claude, and Gemini. Actually, if I’m being truly honest, I have at least 15-20 AI tabs open - multiples of each of those, as well as Grok, NotebookLM, TypingMind, and Google’s AI Studio. I tell myself I am being fair-minded, staying current, comparing. Then I find myself pasting the same context at least three times, re-stating what I’m building, who my clients are, and what “done” looks like. Somewhere between the third paste and the fourth correction, a simple truth shows up: if you want real compounding returns from AI in daily work, you will probably need to choose a home base. Not exclusivity, more like primary residence.
Here is why. Memory and context are the tax you pay every time you switch tools. When an assistant carries forward your preferences, files, and shorthand, you stop babysitting it. You think once, then reuse that thinking. If the assistant also has reach into your stack, you spend less time piping things between apps. As these models become the front door to how we read, write, calculate, and take action online, the center of gravity shifts toward whichever one knows you best and can act on your behalf with the least friction.
So, should you lock in on one platform today? My answer is pragmatic: pick one as your home base, keep the others as trusted specialists. Below is how I’m looking at the big three right now, as both a daily user and someone who builds with them.
ChatGPT as home base: the “do things here” pitch
OpenAI is turning ChatGPT from a chat box into a place where work actually happens. Apps run inside the conversation, with an SDK for developers, and an AgentKit that helps teams build tool-using agents that can plan and execute tasks. Memory exists and is user-controllable, which matters for long-running projects. This is the closest thing to a general hub that can coordinate with third-party services while keeping the user in one flow. For a business user, the practical upside is obvious: fewer tabs, fewer handoffs, more “I asked it, it handled it.” For a developer, the draw is distribution and a clearer path to build in one place and reach lots of end users.
Tradeoffs: you accept a certain center-of-the-universe posture. Distribution lives inside ChatGPT, so your customer interaction stays inside ChatGPT. That can be great for conversion, less great for brand control. On privacy, OpenAI has clear knobs, but you still need to set them and educate teams. And lock-in risk is real if your workflows are only available through one vendor’s app directory.
Claude as home base: the “workbench that remembers” pitch
Anthropic has leaned into continuity for professional teams. Claude now keeps work context across sessions with admin controls, and its Artifacts and file-creation features make it feel like a live workbench, not just a chat. On hard tasks, especially code and complex analysis, Sonnet 4.5 is a monster in the best way. It can also use a computer and tools to get things done, which changes what you delegate. If your day is long documents, board decks, spreadsheet modeling, structured writing, or careful code, Claude is calm under pressure. For builders, the Bedrock route plus Claude’s memory in the API gives you a more controlled enterprise story.
Tradeoffs: fewer consumer-grade integrations in the wild, more usage caps at the top tier, and a smaller “app shelf” for casual add-ons. If your team relies on a wide catalog of plug-and-play mini apps, you may feel constrained. Also, the vibe of Claude is intentionally cautious, which is excellent for legal and enterprise, but sometimes slower to adopt trendy features you see elsewhere.
Gemini as home base: the “Google everywhere” pitch
Gemini’s advantage is proximity to the places you already work if you are deep in Google. NotebookLM can sit on top of your sources and act like a research partner. Gemini 2.5 Pro is a strong thinking model, and the new Computer Use capability gives developers a way to automate real browser actions. If your company lives in Docs, Sheets, Gmail, and Drive, Gemini as the hub can shorten the path from “idea in chat” to “artifact in your folder” in a very natural way. For builders, Google AI Studio and Vertex AI offer the usual enterprise hooks, and the rumor mill points to a next release (Gemini 3) that pushes tool use even further. Google also has deep hooks into most people’s personal lives, including Gmail, Google Search, Google Photos, Google Maps, and more.
Tradeoffs: there is still a split personality between consumer Gemini, Workspace features, and developer offerings. The catalog of ready-to-use add-ins is getting better, but it is less cohesive than OpenAI’s in-chat app push. If you are not a Google-first organization, the glue work can feel like work.
How to decide your home base
Think like a CIO and like a human. As a CIO: where do we already spend our time, what compliance do we need, which vendor reduces switching pain the most, and which gives us real admin control over memory and data. As a human: which assistant makes me repeat myself the least, which one produces work that my colleagues accept without rewrites, which one I enjoy using at 7:30 a.m. when I am behind.
A simple test plan for one week:
Pick a live project and keep it inside a single assistant for five working days.
Turn on memory, set a short project brief the assistant can hold, and talk to it like a teammate.
Push it across your stack. Ask it to draft in your brand template, update a version-controlled doc, enter a task in your tracker, send a calendar hold.
On Thursday, switch to your second-choice assistant and try to continue the project with minimal re-explaining. Take notes on where you got slowed down.
If you build with these tools, make one more test: implement the same tiny agent workflow in each vendor stack and measure developer friction, permission prompts, action reliability, and simple time to “first useful.”
Why not lock in at all
You can keep two models in your pocket without ruining the compounding effect. I treat it like a starting lineup. One home base, two specialists. Claude for careful analysis and long form. ChatGPT for app-mediated flows and multi-step actions. Gemini, when the work is deeply tied to Google or when browser control is the move. The trick is to stop splitting the same project across three tools. Keep a project monogamous, not a career.
What if these assistants become the way we experience the internet
Then two things matter. Portability and scrutiny. Portability means your memories, projects, and prompts should be exportable in plain formats. Scrutiny means you do not let a single assistant filter all of your information diet without checks. Keep a second opinion handy, just not for every step. Think of it like having your primary care doctor and one specialist you trust for a second read.
My plan today
I run most of my daily work in one assistant and keep two others for special jobs I know they handle well. I tell my team which tool is the home base for each project, and we stick to it. That small discipline lowers the context tax and raises the quality bar. It also keeps us honest about where the real advantages are, not just the shiny demos.
I could be wrong, of course. Maybe the best path is to stay flexible and ride the wave of features as they land. Or maybe the biggest win is committing to teaching one assistant who you are, and letting that relationship compound.
What about you: do you plan to choose a home base, or keep rotating across models? If you had to pick one for the next six months, which would you choose and why?
Moving Forward with Confidence
The path to responsible AI adoption doesn’t have to be complicated. After presenting to nearly 1,000 firms on AI, I’ve seen that success comes down to having the right framework, choosing the right tools, and ensuring your team knows how to use them effectively.
The landscape is changing quickly - new capabilities emerge monthly, and the gap between firms that have mastered AI and those still hesitating continues to widen. But with proper policies, the right technology stack, and effective training, firms are discovering that AI can be both safe and transformative for their practice.
Resources to help you get started:
In addition to publishing thought AI leadership on a regular basis, I also work directly with firms to identify the best AI tools for their specific needs, develop customized implementation strategies, and, critically, train their teams to extract maximum value from these technologies. It’s not enough to have the tools; your people need to know how to leverage them effectively.
For ongoing insights on AI best practices, real-world use cases, and emerging capabilities across industries, consider subscribing to my newsletter. While I often focus on legal applications, the broader AI landscape offers lessons that benefit everyone. And if you’d like to discuss your firm’s specific situation, I’m always happy to connect.
Contact: steve@intelligencebyintent.com
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