# Why can a capable AI still be hard to deploy? | AGI Kvist argues that trust and accountability can constrain adoption even when AI capabilities improve. --- Canonical page: https://agiscorecard.com/future-guide/deployable-trust Machine-readable verdicts: https://agiscorecard.com/data.json (CC BY 4.0) This Markdown mirror is generated from the page; the HTML page is canonical. ## Source-linked future guide You can read all sources and guides without JavaScript. Enable it to filter, save or export your own plan. Future guide (/future-guide) / Trust is a practical constraint Trust is a practical constraint Why can a capable AI still be hard to deploy? Kvist argues that trust and accountability can constrain adoption even when AI capabilities improve. Rune Kvist · Latent Space Interview: 2026-09-16 Source review: 2026-10-03 Latent Space · Video · 00:07:58 Rune Kvist Return to the original context. ▶ Load original video Connects to YouTube only after you click. Watch from this chapter ↗ (https://www.youtube.com/watch?v=Sc2_LfWgHb4&t=478s)Publisher’s original page ↗ (https://www.latent.space/p/aiuc) Source, interpretation and uncertainty What does this actually establish? Source status Publisher chapter at 00:07:58 · Not an independently verified outcome Speaker context AIUC CEO · sells AI assurance services What remains open An assurance vendor’s perspective does not prove its service resolves every adoption barrier. AGI editorial interpretation Two ways this could unfold Clear accountability may support adoption. Integration costs and weak task performance can still block it. These are conditional scenarios, not the speaker’s words or a probability estimate. Read alongside: How much permission should an AI assistant have? (/future-guide/agent-permissions) Different framing, dates and definitions; not a direct head-to-head forecast. Where do I stand for now? Lean toward agreement Lean toward disagreement Need more evidence This is your private, device-only record. No public vote count or collective probability is implied. Turn a view into a small experiment What is my next step? A simple plan you can test and revise. Your notes stay in this tab until you save them. What matters to me Understand the change Adapt my work Choose what to learn Explore income ideas Think about education Test my judgment The task or question in my life One action I will try Use the editorial starting point ↗ What would change my view? My review date (optional) I have tried this action Save my next step Export plan Add to calendar Saved on this device. You control the plan and review date; these are not model predictions. Read the interview and evidence on AGI ↗ (https://agiscorecard.com/future-guide/deployable-trust) Embed the free action planner Video and context stay on this source page. ← Return to my next steps (/future-guide#notebook) ✳ JARVIS Research guide What must a buyer know before relying on this workflow? List who reviews errors, who can stop the workflow and what records a user needs. Make my next step ↗ (#plan) Based on published sources and editorial guidance. This is not an AI-generated forecast or a personal capability assessment. A useful reading path A view in its original context A reason to stay open-minded An action you can try No account required. When you want to keep the work Your next step deserves a place to return to. Reading, video access, local saves and exports are free. Use the same plan on another device with AGI’s existing cloud workspace and version history. No paid interview collection or automated personal-report subscription is offered here. AGI cloud membership 9 USDT / 30 days Plus the order-matching decimal and network fees. 50 workspaces, 10 versions per workspace, 64 KB per workspace and 5 MB in total. No auto-renewal or extra AI allowance. Open my plan in membership The member page checks availability. Opening it does not upload your plan or start a purchase; save there explicitly. USDT payment only.