After OpenAI released GPT-5.6 Sol on Thursday, Anthropic reset Claude’s five-hour and weekly usage allowances for all users, presumably to give people more time to use Fable before it leaves Claude plans and moves to the API. Some saw the move as a bid to keep users from defecting to OpenAI’s hot new model. Thibault “Tibo” Sottiaux of the Codex and ChatGPT team quote-posted Anthropic’s announcement with three words: “I smell fear.”
OpenAI and Anthropic are fighting to become the home for agentic knowledge work. OpenAI is merging Codex into ChatGPT. Anthropic is adding a browser to Claude Code and extending Fable access. Power users mix labs anyway. Today we bring you the merge drama, a workflow that puts Fable in charge of cheaper models, and the upside of our model parents fighting: Christmas in July
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Signal
Codex is dead. Long live Codex.
OpenAI found out how much people loved Codex by renaming it ChatGPT.
Five months after releasing Codex as a standalone app, OpenAI folded it into the new ChatGPT desktop app last week and relabeled the previous ChatGPT app “ChatGPT Classic.” The new app has three modes: Chat for questions, Work for longer assignments across tools, and Codex for developer workflows.
We’re on the record as loving Codex. We wrote a whole guide about it and rebuilt many of our workflows around it. But since the merge, we’ve barely noticed. Coders are still coding in Codex. I toggled into Work during a meeting, kept drafting, searching files, and pulling from Slack, and didn’t realize until later that I’d switched.
You wouldn’t know that if you were on X that day. Backlash to the move among Codex lovers was immediate and vocal. Developer and YouTuber Theo Browne called the merge a “generational fumble.” Redditors complained of duplicate apps, buried chats and projects, broken plugins, and unclear limits. One thread called the release and the communication about what was happening with Codex “mayhem.” The company appears to have decided that alienating some power users is worth potentially gaining a larger audience for Codex: ChatGPT has more than 800 million weekly users—orders of magnitude larger than Codex’s 5 million weekly users. The merge is a bet that the features that made Codex so compelling for its fans, like file access, tool use, and the ability to carry out tasks across long time horizons and multiple turns, will reach more people inside a product they already know.
OpenAI isn’t the only frontier lab experimenting with how it packages its products. After launching Cowork on Claude Code’s agentic architecture to win over more knowledge workers in January, Anthropic has now put Chat and Cowork in one “home” tab. Both moves aim to introduce chat interface users to the possibilities of AI agents.
Underlying these changes is a shared bet that agentic work is where the technology is moving. Both labs want their flagship assistant to become the place where any knowledge-work task begins: Give the model files, tools, and a sustained assignment, then let it act. Whether OpenAI or Anthropic succeeds will depend on how well their general-purpose apps serve newcomers while preserving the control, context, and reliability power users expect.
What to do this week: For Codex lovers, switch to ChatGPT Codex and carry on as usual. But if you’re agent-curious and haven’t made the leap to agent-driven interfaces, here’s how to get started in Codex: Give it one defined assignment using your files and a concrete deliverable. Require approval before Codex sends messages or changes anything outside the app. You can test agentic work without first learning how to use a coding agent.
Steal this workflow
Put fancy models in charge of affordable ones
During our GPT-5.6 launch livestream, ChatPRD founder Claire Vo described her relationship with Fable 5: “What Fable does is none of my business.” She can say that because she treats Anthropic’s most expensive model as a senior consultant instead of a daily driver. Fable plans the job, delegates bounded tasks to cheaper models, and reviews what comes back.
Claire is part of a broader trend among power users who use the smartest, priciest model as the boss and let cheaper models do the grunt work. Every CEO Dan Shipper takes the pattern one step further by crossing model labs, which requires Claude and Codex to share the same brief and project files. Fable leads. GPT-5.6 Sol executes.
Here’s how to set it up, first for working with models within the same family, then for working across different models:
1. Easier: Fable → Sonnet inside Claude Code. Ask Claude Code to create .claude/agents/sonnet-worker.md in your project. The file registers a Sonnet worker—the cheaper, less capable model—that Fable can call:
Restart Claude Code or run /agents to load the worker. Start on Fable: Describe your task and then tell it, “Write an implementation brief, delegate it to sonnet-worker, and review the changed files and test results before you finish.” Fable plans the work, opens a separate Sonnet context to execute it, and receives the result for review.
2. Advanced: Fable → Sol across Claude and Codex. This setup lets Claude and Codex hand work to each other. Fable is a strong planner and reviewer, while Codex is a capable implementer. You stay in one conversation with Fable and give it a task you’ve already thought through. It delegates the coding to Sol—like an editor who owns a story, assigns the draft to a writer, and reviews it when it’s completed.
To wire it up, install and sign into both command-line tools and open Claude Code in the shared project. Ask Claude Code to create .claude/skills/sol-worker/SKILL.md:
Then tell Fable: “Use the Sol worker to implement the settled brief. You own the plan, any scope changes, and final review.” Fable writes its brief to a shared file, launches Sol in the same project directory, and returns the changed files for our review. You never leave Claude Code. Sol does the implementation work in the background.
Do this week: Give one AI worker a small assignment with a settled spec and an objective check—for example, “Add CSV export to this dashboard and run the existing tests.” If Fable has to redo most of the output, the cheaper model is not saving you anything yet.
Data point
53 percent
How much an AI agent’s error rate fell in a new paper after it stopped rewriting code from scratch for steps it had performed before. Instead, the agent saved what worked as a reusable tool. When it encountered the problem again, it used the tool. Once your agent figures out a recurring task, keep the method and let the results compound into a better outcome next time.
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What we’re reading
- “AI 2040” is a new policy scenario from the AI Futures Project. The group’s earlier report, “AI 2027,” released last year, imagined AI automating AI research and reaching superintelligence within a year. AI 2040 proposes a slower alternative: The U.S. and China make AI research public, hold development at roughly human-expert capabilities, and delay superintelligence until 2040.
- “AI-Native Firms”, a working paper from INSEAD’s Hyunjin Kim and Harvard Business School’s Rembrand Koning, finds that AI-native startups are 25 percent smaller than non-AI startups in the same industry and time cohort, with more engineers and fewer junior workers and managers.
- “The Future Worth Building Is Human” is the thesis behind what Thinking Machines Lab is building: customizable models and interfaces that users can shape over time. The post argues that this approach is necessary because much of an organization’s valuable knowledge is tacit and local—and therefore difficult for a general-purpose model to capture.
Discuss
“this is like your parents fighting and getting two christmases”—X user Sophie, after Anthropic extended Fable access through July 19
Anthropic has pushed Fable’s paid-plan cutoff from July 7 to July 12 to July 19 and kept Claude Code’s weekly limits 50 percent higher than standard. The model wars have an upside: Users are getting higher limits, longer promotions, and more included access. But Christmas always ends. Use the bonus time to benchmark Fable. Decide which work justifies the expensive model before the next deadline moves—or doesn’t.
Katie Parrott is a staff writer. She writes Working Overtime and contributes to Vibe Checks, Source Code, and Context Window. To read more essays like this, subscribe to Every, and follow us on X at @every and on LinkedIn.
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