The Vertical AI Bubble: We Keep Forgetting That LLMs Roll Dice
AI for customer support in a specific niche of healthcare billing. Every pitch deck looks the same: take a foundation model, wrap it in a narrow workflow, add some proprietary data, call it a moat, and raise a Series A at a valuation that assumes this thing behaves like software.That last part is where I think we’ve collectively lost the plot. Reconcile the ledger.The word enough is doing an enormous amount of work in that sentence, and I don’t think most people building these companies have sat with what it actually means.Traditional software is deterministic.
- ▪AI for customer support in a specific niche of healthcare billing.
- ▪Every pitch deck looks the same: take a foundation model, wrap it in a narrow workflow, add some proprietary data, call it a moat, and raise a Series A at a valuation that assumes this thing behaves like software.That last part is where I t
- ▪Reconcile the ledger.The word enough is doing an enormous amount of work in that sentence, and I don’t think most people building these companies have sat with what it actually means.Traditional software is deterministic.
Hacker News (AI / LLM) files mainly under ai. We currently carry 4,488 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Medium |
| Canonical URL | https://medium.com/@MirArshadTalpur/the-vertical-ai-bubble-we-keep-forgetting-that-llms-roll-dice-2ac707f91c61 |
| Publication time | Tue, 11 Aug 2026 15:36:45 +0000 |
| Retrieval time | 2026-08-11T15:45:45.251Z |
| Last seen | 2026-08-11T15:45:45.251Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | XpapLu8ERPVs · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
AIAI AgentLLMArtificial IntelligenceThe Vertical AI Bubble: We Keep Forgetting That LLMs Roll DiceMir Arshad Ali13 min read·Just now--ListenSharePress enter or click to view image in full sizeDeterminsitic building with Probabilistic FoundationsI’ve spent the last year watching an entire generation of vertical AI startups get funded, launched, and celebrated. AI for law firms. AI for insurance claims. AI for radiology. AI for accounting. AI for customer support in a specific niche of healthcare billing. Every pitch deck looks the same: take a foundation model, wrap it in a narrow workflow, add some proprietary data, call it a moat, and raise a Series A at a valuation that assumes this thing behaves like software.That last part is where I think we’ve collectively lost the plot.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Medium.
Discussion
0 commentsMore from Medium
