← All WritingAlephic / Writing<br>Things I Think I Think About AI (2026 Edition)<br>In the 2026 edition of Things I Think I Think About AI, I revisit AI harnesses, top models like Fable, and agent-first workflows reshaping the field.<br>Noah Brier/August 6, 2026/6 min read<br>ShareSubscribe

Last year I published a list of 29 things I think I think about AI, and I thought it might be good to revisit. So here’s my 2026 edition:<br>Everyone is going to be working in harnesses (Codex, Claude Code, etc.) in the future.<br>I'm not sure if that harness-first future is 6, 12, 18, or 24 months out.<br>If your primary experience of AI is still in ChatGPT, Claude, or Copilot, you have no idea what these models are really capable of.<br>Don't count out Google and Microsoft in the harness race quite yet. I still think the fundamental question is whether these model providers can build great interactive surfaces before the interactive surface providers can figure out how to properly integrate models.<br>Anyone who says AI can't do useful work is lying to themselves or to you.<br>Fable is the best model on the market, and I don't think it's close.<br>Fable is the first model that feels smart enough that you shouldn't waste its time having it write simple code. Its best use is as a planner.<br>There are only three real vectors to judge models: Raw intelligence (frontier foundation models), cost-per-intelligence (value models), and tokens-per-second (there's some minimum bar for intelligence here, but it's low), everything else is competing for scraps. The frontier foundation models are where we all pay the most attention (Fable, Opus, Sol, etc.), but I think the most interesting/dynamic space right now is cost-per-intelligence, where you have Gemini Flash-Lite, 5.6 Luna, and open source models like DeepSeek Flash v4. These are workhorse models and play a huge role in almost any large-scale AI project.<br>I still think the hype around local models is far from the reality. The only thing I find myself using them for at all is a tiny bit of local search/embeddings stuff and almost nothing else. The good open source models are too big to run locally, and everything is just too slow to make it worth it (before you ask, I have an M5 Max with 128 GB RAM and a DGX Spark in my home rack).<br>Codex Desktop (now ChatGPT) is awesome and lowered the barrier to entry on harnesses in a really powerful way. I don't understand why everyone isn't using it all the time.<br>The tokenmaxxing conversation is stupid: the vast majority of companies should be spending way more on AI, not way less.<br>The best way to learn AI is to buy a $200 Claude or OpenAI plan and try to exhaust your limits.<br>There are a lot of companies that are trying to optimize their AI spend before they have any. That is a dumb mistake that will come back to bite them when, in a year, they still have no one using AI.<br>While token leaderboards are a blunt object, generally tracking adoption and making sure teams are using the technology is a good thing to do. Obviously it can create bad incentives, but the more damaging incentive is individuals doing nothing.

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