The changes OpenAI and Anthropic made to their products in the last two weeks worried me more than anything else this year.
This isn’t a worry about safety, the loss of human autonomy, or anything deeply existential. Instead, the worry stems from the idea that these changes feel like a shortcut to user growth that can come with deep repercussions.
The short version: agents just got easier to start and no easier to use well, and the bill for that difference will land on the people I think can benefit from the technology the most.
For context, OpenAI and Anthropic have each built wildly successful products that have allowed users to go from simply chatting with an LLM to working alongside agents.
For OpenAI, the most popular harness for this was, then is, Codex.
For Anthropic, their equivalent was Claude Cowork (for non-technical users) and Claude Code (for more tech-savvy users).
These platforms allowed users to see the real capability of giving agency to an AI. Having agents build morning briefs from data in your tech stack became a basic task, and teams were developing entirely new ways for AI to take real work off their plates.
So what was the issue?
I believe it was a product positioning problem. Both first developed primarily coding-focused harnesses (Codex and Claude Code) but quickly needed to figure out how to bring the same power and capability to non-engineers.
Anthropic took the path of creating Cowork - essentially the equivalent of Claude Code, but with a more tailored UI for non-technical users. OpenAI simply adapted Codex to present those use cases in the same product.
Neither was optimal.
Anthropic users were left with figuring out whether to use Chat, Cowork, or Code on their own. OpenAI users were left not realizing all that they could do with Codex, a tool that, even by its name, is not primarily inviting to a non-technical user.
So what changes did they both make around the same time that didn’t sit right with me?
Consolidation!
Anthropic has moved Cowork closer to Chat, making the transition from one to the other as small as possible.
OpenAI, on the other hand... had quite a few days. They renamed and reshuffled things twice in the same week, and even people who followed this daily lost the plot. That created quite the confusion, and has now left users with ChatGPT Chat (not a pop-up?), ChatGPT Work, and Codex...
Long story short... both made (questionable) product decisions to do one thing. Improve the simplicity and accessibility of using Agents.
Credit where credit is due - what they have built is incredible. In the workshops I run for teams that are focused on helping knowledge workers use these frontier models and harnesses, it’s a breath of fresh air not having to teach people about the terminal.
But I’ve also seen that as these tools have become more “user-friendly”, the underlying issues stay the same:
they don’t know what can be done with these agents
they don’t know the optimal way to organize context and orchestrate agents
they don’t know how to evaluate the work that agents do
they don’t know the risks behind using these platforms
There is still a massive need to enable people to use this technology efficiently and effectively, which simplification doesn’t solve.
And with the most recent changes, the simplification has almost gone too far. With a single click, without the right guardrails, far more is on the table in terms of risk that most don’t recognize (runaway spend, poorly evaluated work, and misunderstanding of what the value of AI is).
Here’s the simplest flow of logic I can give you on all of this:
Agents are now easier to access and set up than ever
Guidance on how to use them is lacking. People don’t know what they’re even using behind the scenes.
Agentic workflows burn orders of magnitude more tokens than chat, and untrained users multiply that with retries, bloated context, and poor model choice.
More use of agents in inefficient manners means more tokens are burned.
More tokens burned means more budgets being blown.
More budgets being blown means more CFOs asking the question of ROI.
More questions of ROI lead to uninspiring answers - because without the proper guidance and enablement, we’ll keep seeing Fable 5 being used to summarize simple meeting notes and generate hallucinated, slop-filled follow-up emails people don’t read.
More bad uses of AI lead to more constraints on users to experiment.
Less experimentation leads to lost opportunities to change how work gets done.
So how does this get solved? I’m biased, of course, but I’ve seen first-hand how powerful focused, smooth, and steady enablement that takes non-technical users under the hood can be. Marketing and finance teams learning when and why to reach for certain models, how to orchestrate subagents efficiently, how to plan the work before an agent fires off thousands of tokens... that’s the version of this that people get excited about, but it doesn’t magically come in the box.
What you get out of these tools has and still comes down to judgment and taste. And while access is now one click, those key elements aren’t and won’t be.
For a receipt showing I wrote this myself, key by key, on Organic: writeorganic.app/p/39XW-63Z5