# Uncertainty, source, and progress cues in AI UI

> Show uncertainty, source grounding, and visible progress during AI tasks so users can judge whether the system is thinking clearly or just stalling.

- Canonical HTML: https://growth.iangoh.com/growth-ideas/uncertainty-source-and-progress-cues-in-ai-ui/
- Source: [newsletter.posthog.com](https://newsletter.posthog.com/p/what-we-wish-we-knew-before-building)
- GrowthDex source hub: [PostHog Newsletter](/sources/posthog-newsletter-newsletter-posthog-com-2/)
- Last checked: 2026-05-26
- Rarity: rare
- Budget: low
- Channels: Product, Conversion, Retention
- Stages: ai products, trust, ux, retention


## Why this can grow

Trust in AI products falls apart when the system fails opaquely. Users do not only care whether a task eventually finishes. They care whether they can see what the system is using, how confident it seems, and whether it is still making progress. Those cues reduce panic during slow operations, make wrong answers easier to challenge, and help the product feel accountable rather than theatrical.

## Ian's take

From scaling consumer platforms across MENA and Southeast Asia, my default is to distrust growth work that only looks good in a slide. My bias is to treat this as a small market test first. Make the audience narrow, make the promise concrete, and let the first real response decide whether it deserves more work. For retention, I would watch the second and third use, not just the first click. A tactic is real when it changes a habit. For this tactic, I would watch one clear growth signal before putting more time or budget behind it.

## Action plan

1. Define one narrow startup segment where uncertainty, source, and progress cues in ai ui can create a measurable lift.
2. Turn the tactic into one offer, page, campaign, or workflow for the Product and Conversion channel.
3. Use the evidence from newsletter.posthog.com to set the first version of the message, format, and audience.
4. Launch a small test for 7 to 14 days with one success metric: one measurable growth signal.
5. Review the result, keep the winning message, remove weak variants, and turn the learning into a repeatable growth playbook.

## Source-backed example

PostHog says some of the most common complaints about agent UX were inconsistent performance, unclear capabilities, generic errors, and a lack of signs of uncertainty, source of insights, and progress.

## Adjacent tactics in the same lane

- [Weekly traces hour for agent quality](/growth-ideas/weekly-traces-hour-for-agent-quality/) - same source, 2 shared channels, 2 shared stages
- [Task-based model routing for AI speed](/growth-ideas/task-based-model-routing-for-ai-speed/) - same source, 2 shared channels, 2 shared stages
- [AI response rating with follow-up context](/growth-ideas/ai-response-rating-with-follow-up-context/) - same source, 2 shared channels, 2 shared stages
- [Workflow-first AI demand validation](/growth-ideas/workflow-first-ai-demand-validation/) - same source, 2 shared channels, 1 shared stage

## Read GrowthDex essays

Browse the plain-English essay index at [GrowthDex Blog](/blog/).

## Related GrowthDex essays

- [AI products stop feeling smart when they hide their context](/blog/ai-products-stop-feeling-smart-when-they-hide-their-context/) - AI products, product-led growth, brand trust

## Advisory

If you want help turning this into a working growth system, Ian Goh offers advisory at https://iangoh.com/advisory.