---
title: My changing AI opinions
date: 2026-06-05T09:41:33+08:00
categories:
  - llms
description: I used Claude to audit my evolving AI opinions, documenting my shifts toward specialized SLMs, multi-agent workflows, and MCP. I contrast my changing stances on vibe-coding and human intervention against constants like code-driven arithmetic.
keywords: [claude, mcp, slm, agents, vibe coding, llms, model context protocol]
---

I asked Claude about my AI opinions.

> Based on my transcripts and blog posts, find the three claims I make most consistently, the three I've quietly reversed, and the one assumption I've never questioned but everything depends on.

<!-- https://claude.ai/chat/1d41b5a3-7963-4e87-aade-c44a37d5f355 -->

Here are things I've changed my opinion on:

1. **THEN**: One frontier model will win - not specialization. **NOW**: Gemini for media, Claude for strategy/style, GPT for rigor. SLMs as tools.
2. **THEN**: Carefully curate my course content. **NOW**: Give students prompts directly.
3. **THEN**: Web apps are differentiated artifacts. **NOW**: HTML is easier to generate than PPT - a signal of slop, not craft.
4. **THEN**: Human in the loop. **NOW**: Human NOT in the loop, bottlenecking it. On-the-loop, etc. is fine.
5. **THEN**: Minimal single-agent loop, avoid sub-agents" **NOW**: Multi-agent, sub-agent, and agent teams.
6. **THEN**: Avoid MCP, prefer SKILLS.md. **NOW**: Use MCP because integrating with Claude / ChatGPT / ... is easy.

There are the top contradictions in my opinions.

1. "Vibe code everything, end-to-end" _vs_ "Don't commit based on vibe-coding, it's not fully reliable".
2. "Experience is a liability" _vs_ "Domain expertise is an edge"
3. "Paid plans are fine for private data" _vs_ "Don't assume paid = private"
4. "Get out of the way / delegate everything" _vs_ "Verification is the whole job"
5. "Build, don't plan" _vs_ "Plan → Correct → Execute"

Some things, I have never questioned.

1. For numbers, math, or correctness, make AI write and run code -- never trust prose arithmetic.
2. Judge AI against human accuracy, never against perfection -- because experts disagree among themselves.
3. Use AI heavily -- reach for it first, high volume.
4. Don't build a foundation model from scratch -- steer existing general models instead.

Things I usually say, **but** there are exceptions.

1. "Always verify". **But** blindly trust AI in non-core areas where you are unskilled (such as personal finance).
2. "AI is improving fast". **But** there is a jagged edge, to verify before upgrading.
3. "Don't build models". **But** go ahead if it's easy and beats LLMs clearly.
4. "Models keep getting cheaper". **But** not in May 2026.

![](https://files.s-anand.net/images/2026-03-05-my-changing-ai-opinions.avif)