OpenAI is building AI agents for everything. Will everyone use them?

Part of that lead is getting the product-market fit right, and part comes from complaints about safety restrictions on Anthropic’s models and compute shortages. OpenAI’s steps toward more human-centric harness continue with ChatGPT Work, but the engineers I spoke to insisted the key differentiator is the strength of OpenAI’s latest powerful and cost-effective models. 

“The frustrating answer is that a lot of times it is the model, and one thing that we have tried to do really well with this app is fully leverage the model,” Ambrosino said. 

That explanation returns to the “bitter lesson” learned by AI researchers that a better general model is more important than specific domain experience. For true believers, the harness is a temporary crutch, not the moat.

“You could get good results in the short term by adding a whole bunch of extras—if and thens and tools—but like, come on, the next model is going to come out in a couple of months and make that obsolete,” Gershenson told TechCrunch. His team focuses on the simplest ways to expose the model to the tools and context it needs—and no more. 

“The goal of good harness engineering is to… be more precise about what information the model really needs to solve your problem, because the models are getting better and better at doing that if you simply let them do their thing,” Gershenson said.

There is an open question, though, if most people or models are ready for that. Ethan Mollick, the Wharton School of Business professor who studies AI tools in the workplace, still sees Claude as more user-friendly, writing that “ChatGPT tends to want to do magic & just do it for you, while Claude does comparisons & shows them, repeatedly asking for input & feedback and doing A & B tests.”

Sottiaux, and perhaps OpenAI at large, disagree, arguing that the conversational nature of the app is better than learning how to use an application. “We definitely see that the world seems to be ready,” he says of the app. “This is why we’ve had incredible adoption.”

Still, it’s not clear that a model-specific harness is even the right bet for maximizing a model. Comparisons run by companies like Composio and Databricks show that different harness and model combinations deliver different performance on coding benchmarks. Databricks found that Pi, an open-source harness published by the software company Earendi, outperformed Codex while using the same GPT 5.5 model. Pi has been used to build projects like OpenClaw and CloudflareOS. 

Pi’s creator, Mario Zechner, says his intentionally minimalist harness is evidence that an AGI-pilled approach can work, at least for software engineers and coding tasks. What it lacks in explicit features, he says, is made up for by its ability to modify itself and build its own interfaces. He sympathizes with the challenge that OpenAI’s engineers face in expanding their user base beyond engineers.

“Everything is coding agent shaped…the reason is that they only have training data for coding agent tasks,” he told TechCrunch. “Say I’m in management, I make a decision today, and the outcome happens months later. You cannot capture that in a simple trace of a user and agent back and forth, so all of these kinds of tasks and anything that you don’t digitize is inaccessible to a model to learn.”

Like other open source providers, he sees the big lab’s effort to push their harnesses as a way to lock-in users; “They need to own the entire stack; otherwise, they just become a model provider and then need to compete with Chinese models.” 

He and other engineers TechCrunch spoke to felt that the insight into token spend and agent behavior in frontier labs’ harnesses is too limited. In a sense, that’s less meaningful to non-technical workers, but as with the coding tools, uptake at the scale OpenAI hopes for will eventually force harder conversations about cost. 

For example, messing around on a $20-a-month subscription, I used more than 80 million tokens in four days, which cost $65, according to the model’s analysis (there’s no dashboard in the app). That’s a subsidy of more than 3x the subscription price for four days of casual use alone.

“We are working every day to push the frontier on efficiency,” Sottiaux said, pointing to a recent 80% price cut for users of OpenAI’s Luna model. “If you wake up six months from now, you should be able to do all of the same with less spend.”

The other relevant question is whether these apps create a lock-in effect on customers through data retention, or the sheer pain of configuring access to all the plug-ins and their permissions. 

Inside OpenAI’s wood-panelled, plant-filled headquarters, which I visited in July, the atmosphere was calm but slightly tense; these are people with a lot to do. The engineers I spoke with were constantly monitoring their laptops as we talked, and rushing from meeting room to meeting room. 

Nathan, the head of the product engineering team, said the focus remains on “the promise of the magic box, but I still think there’s too much complexity…I’m very optimistic that we can solve it, with the model and in a truly AI native way.”