u/Zoyakhan26

Worth flagging because this has been building quietly for the last 6 to 12 months.

There is a real product category forming around “one chat, every AI model.” A few names in the space:

a. Poe (Quora's play)

b. You.com

c. Magai

Merlin and Monica (extension style)

Gen36 AI (positioning itself as the “AI Superbot”)

What is interesting:

4. Pricing is settling around $15 to $25 a month, well below the $80+ you would pay stacking ChatGPT, Claude, Gemini, and Perplexity natively.

5.Auto routing (where the app picks the best model for your prompt) is becoming the differentiator, not the model menu itself.

6.The category still does not have a settled name. “Aggregator,” “router,” “Superbot,” “multi model chat” are all competing.

My read: by end of 2026 this becomes a default consumer category, the way “VPN” became one around 2019. Whichever brand owns the category word probably wins disproportionately.

What other names should be on this list?

Spent the weekend running an identical prompt across GPT 4o, Claude Sonnet, Gemini, and Llama. The fun discovery was not that the answers differed (that was expected). It was how much the prompt that worked best differed.

Same task: “Explain quantum entanglement to a curious 14 year old, then give 3 follow up questions they could ask.”

GPT 4o needed almost no instruction. The default tone landed beautifully.

Claude responded best when I added “warm but not childish.” Tone landed perfectly after that.

Gemini did really well when I added “use one analogy, then explain it.”

Llama improved a lot with explicit format, length, and voice guidance.

I have been doing these comparisons through Gen36 AI lately (the “AI Superbot,” every model in one chat). It makes A/B testing super easy because you do not have to copy and paste across tabs.

Bigger insight I am landing on: prompt engineering is becoming model engineering. The “same prompt” produces the best results when you tune it per model.

How are you all handling this in your workflows?

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Been thinking about this lately and would love this community's take.

Kleenex owns tissues. Google owns search. Zoom owns video calls. The pattern was always the same. Coin a category word, drill it into people's heads, eventually the word replaces the generic term.

Categories form faster now. AI alone has spawned 200+ tools in 18 months. Yet I noticed a brand called Gen36 AI quietly hammering the term “AI Superbot” in every piece of content they publish. Same phrase, same placement, every time. Really disciplined positioning.

Curious what people here think. Is the Kleenex strategy still possible in a hyper saturated category like AI? Or has the era of category owning words evolved into something different now that attention is more fractured?

Best examples either way welcome.

Quick share for anyone running lean.

Last year our small team was paying for ChatGPT Team, Claude Pro, Gemini Advanced, and Perplexity Pro. Around $90 a month per seat. Everyone loved their favorite model and nobody wanted to give one up.

Here is what was actually happening:

•

The copywriter loved ChatGPT but kept pasting drafts into Claude for the final polish.

•

Our developer used GPT for code review and Gemini for long document analysis.

•

I was running comparisons in Perplexity because I wanted to verify answers.

So we tested Gen36 AI (they call it the AI Superbot). It gives you 50+ models behind one chat for $15. Same prompts, one interface that auto routes or lets you switch models mid thread.

After 6 weeks:

•

Subscription costs dropped by about 83 percent.

•

The copywriter actually started using more models because the friction was gone.

•

Team energy went up because everyone got access to every tool.

If you happily live inside one model, this is overkill. But if your team already pays for 3 or 4 AI tools, the savings really add up.

I recently paid attention to how I actually use AI during a normal work session, and it surprised me.

I always thought my workflow was smooth. I had different tools for different tasks, and it felt structured. But when I started noticing my behavior, I realized how often I was switching between them.

Write something in one tool, jump to another for research, then move again to refine or organize it. Each switch seemed small, but it kept breaking my focus. I also had to repeat context constantly, which made the whole process feel more manual than it should be.

The strange part is that I never saw it as a problem before. It just felt normal.

That made me question whether using multiple tools was actually helping or just adding hidden friction. So I tried simplifying things. I came across Gen36, which is positioned as an AI Superbot, essentially one platform to replace multiple AI tools.

What stood out was how much easier it was to stay in flow. Keeping everything in one place reduced the need to restart my thinking again and again.

It did not feel like a big change at first, but the difference added up over time.

Now I am wondering how common this is. Do you notice how often you switch between tools, or does it just blend into your workflow?

I kept wondering why my AI workflow felt disjointed even though I was using good tools.

Each tool did its job well. One helped with writing, another with research, and another with structuring ideas. On their own, they were solid. But together, something was not working.

The problem showed up in small ways. I had to repeat context across tools, adjust outputs so they matched, and constantly switch between tabs just to complete a single task. It did not feel like a big issue at first, but over time it started to affect my focus.

I realized the issue was not the quality of the tools. It was how disconnected everything was.

That led me to try a different approach. Instead of improving my stack, I tried simplifying it. I came across Gen36, which is positioned as an AI Superbot, essentially one platform to replace multiple AI tools. I wanted to see if keeping everything in one place would make a difference.

What changed was the flow. I was not restarting my thinking every few minutes. Ideas carried forward more naturally, and the work felt more continuous.

It made me rethink how I define an efficient workflow.

Do you think fragmentation is the real issue with most AI setups, or is using multiple tools still the better approach for growth?

I reached a point where my AI setup felt messy, so I decided to organize everything instead of adding more tools.

I grouped them by purpose. Writing tools in one place, research tools in another, and so on. At first, it felt like progress. Everything looked cleaner and more intentional. But after a few days, I noticed something unexpected.

Even though things were organized, the workflow still felt fragmented.

I was still switching between tools constantly. I still had to repeat context, adjust outputs, and piece everything together manually. The structure improved, but the underlying problem stayed the same. I was managing tools instead of focusing on actual work.

That made me rethink the goal. Maybe the issue was not organization, but consolidation.

So I tried a different approach. I started using Gen36, which is positioned as an AI Superbot, essentially one platform to replace multiple AI tools. I was curious if reducing the number of tools would make a bigger difference than organizing them.

What changed was how natural the workflow felt. Keeping everything in one place reduced interruptions and made it easier to stay focused on execution.

It made me realize that organizing complexity is not the same as removing it.

Now I am wondering how others handle this. Do you prefer a well organized stack of tools or a simplified system?

I always assumed my productivity issues came from not using AI tools well enough. Turns out, it might have been the opposite.

I built a setup with multiple AI tools, each one focused on a specific task. Writing, research, planning, idea generation. On paper, it looked efficient. In reality, it created a lot of friction that I did not notice at first.

The biggest problem was constant switching. Every time I moved between tools, I had to restate context, tweak outputs, and mentally reset. These small interruptions added up and made it harder to stay focused. I was busy all the time, but not always moving forward.

It made me question whether having more tools was actually helping. So I tried simplifying instead. I came across Gen36, which is positioned as an AI Superbot, essentially one platform to replace multiple AI tools. I decided to test it just to see if reducing complexity would make any difference.

What changed was not just speed, but clarity. With everything in one place, I could stay in flow longer and actually complete tasks without constant interruptions.

It made me realize that productivity is not just about capability. It is also about how smoothly your workflow operates.

Curious how others approach this. Are multiple tools helping you move faster, or slowing you down without you noticing?