The ChatBubble: Stop Bolting. Start Building.
Call it the ChatBubble.
Chat gets stapled onto products because it seems cheap—quick to ship, subsidized to run, and easy to market. The reality bill comes later. That’s the bubble.
- Feels cheap to implement: one API call and a chat drawer = “AI shipped.” Hidden bill: product debt, messy UX, and support load.
- Feels cheap to run: credits and discounts hide real usage costs. Hidden bill: latency, monitoring, evals, compliance, sprawl.
- Feels cheap to market: “Now with AI!” looks good in a slide deck. Hidden bill: churn, because ChatGPT can replicate your feature in 60 seconds.
The compute vendors capture the margin. Subsidies mask fragility. When they unwind, so will copy-paste AI features.
Make the product smarter, not the user busier.
Chat isn’t the final form of AI. ChatGPT was a breakout experiment, not a decree that conversation fits every job. A general chat box is a blank canvas—it rewards a few power users and burdens everyone else.
Three models, one principle: augment without new behavior
- Whiteboard (general chat): infinite potential; the user invents the workflow.
- Calculator: complexity hidden; clear buttons → instant outcomes. Nobody writes
sin(x)—they press sin. - Bike drivetrain: effort multiplied without instruction; customize if you care, otherwise just pedal.
Bring that feeling to software. Example: “We noticed this server sits at ~20% CPU. A safer, cheaper instance is prepped—review, explain-diff, and one-click confirm with automatic rollback.” That’s not “chat with our app.” That’s embedded judgment in the flow.
Wrappers vs. woven-in
Wrappers (fragile):
- Docs sidecar: “Ask our docs” forwards your prompt to a model.
- Chat-your-CRM: rephrases clicks you already know how to do.
- Blank AI drawer: an empty chat inside your product.
If ChatGPT can do it in 60 seconds, it isn’t a durable feature.
Woven-in (defensible):
- Billing controls: auto-flag abnormal spend, draft policy update, route for one-click confirm.
- Support triage: classify → propose safe actions → draft replies, held for one-click confirm.
- Fulfillment QA: detect out-of-spec orders, generate remediation steps, stage fixes for one-click confirm.
No new behavior to learn. Fewer steps, same interface.
Operating in the ChatBubble
As cheap money tightens and compute meets P&L reality, the subsidy unwinds. The work that survives is the work that pays.
Before you ship an “AI feature,” ask:
- Will this move a P&L line this quarter? (Lower COGS, faster cycle time, higher conversion, lower churn.)
- Would we still ship it if credits weren’t subsidized?
- Does it lean on our moat? (Workflow depth, trust/compliance, distribution, proprietary feedback loops.)
Then run what actually wins: reliability over spectacle, evals over vibes, cost as a first-class metric, and feedback loops by design.
The ChatBubble will pop—and that’s healthy. What remains are teams shipping outcomes, not wrappers; invisible, specialized intelligence that compounds inside real workflows.
Build what still makes sense when credits aren’t cheap. Make the product smarter, not the user busier. Stop bolting. Start building.
Concept framed by me, draft generated with AI.